Instruction: What field is the article from?

Input:
Title: LanGWM: Language Grounded World Model
Abstract: Recent advances in deep reinforcement learning have showcased its potential
in tackling complex tasks. However, experiments on visual control tasks have
revealed that state-of-the-art reinforcement learning models struggle with
out-of-distribution generalization. Conversely, expressing higher-level
concepts and global contexts is relatively easy using language.
  Building upon recent success of the large language models, our main objective
is to improve the state abstraction technique in reinforcement learning by
leveraging language for robust action selection. Specifically, we focus on
learning language-grounded visual features to enhance the world model learning,
a model-based reinforcement learning technique.
  To enforce our hypothesis explicitly, we mask out the bounding boxes of a few
objects in the image observation and provide the text prompt as descriptions
for these masked objects. Subsequently, we predict the masked objects along
with the surrounding regions as pixel reconstruction, similar to the
transformer-based masked autoencoder approach.
  Our proposed LanGWM: Language Grounded World Model achieves state-of-the-art
performance in out-of-distribution test at the 100K interaction steps
benchmarks of iGibson point navigation tasks. Furthermore, our proposed
technique of explicit language-grounded visual representation learning has the
potential to improve models for human-robot interaction because our extracted
visual features are language grounded.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach
Abstract: We propose the Kuramoto Graph Neural Network (KuramotoGNN), a novel class of
continuous-depth graph neural networks (GNNs) that employs the Kuramoto model
to mitigate the over-smoothing phenomenon, in which node features in GNNs
become indistinguishable as the number of layers increases. The Kuramoto model
captures the synchronization behavior of non-linear coupled oscillators. Under
the view of coupled oscillators, we first show the connection between Kuramoto
model and basic GNN and then over-smoothing phenomenon in GNNs can be
interpreted as phase synchronization in Kuramoto model. The KuramotoGNN
replaces this phase synchronization with frequency synchronization to prevent
the node features from converging into each other while allowing the system to
reach a stable synchronized state. We experimentally verify the advantages of
the KuramotoGNN over the baseline GNNs and existing methods in reducing
over-smoothing on various graph deep learning benchmark tasks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications
Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across
various natural language tasks, marking significant strides towards general
artificial intelligence. While general artificial intelligence is leveraged by
developing increasingly large-scale models, there could be another branch to
develop lightweight custom models that better serve certain domains, taking
into account the high cost of training and deploying LLMs and the scarcity of
resources. In this paper, we present MindLLM, a novel series of bilingual
lightweight large language models, trained from scratch, alleviating such
burdens by offering models with 1.3 billion and 3 billion parameters. A
thorough account of experiences accrued during large model development is
given, covering every step of the process, including data construction, model
architecture, evaluation, and applications. Such insights are hopefully
valuable for fellow academics and developers. MindLLM consistently matches or
surpasses the performance of other open-source larger models on some public
benchmarks. We also introduce an innovative instruction tuning framework
tailored for smaller models to enhance their capabilities efficiently.
Moreover, we explore the application of MindLLM in specific vertical domains
such as law and finance, underscoring the agility and adaptability of our
lightweight models.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: CERN for AGI: A Theoretical Framework for Autonomous Simulation-Based Artificial Intelligence Testing and Alignment
Abstract: This paper explores the potential of a multidisciplinary approach to testing
and aligning artificial general intelligence (AGI) and LLMs. Due to the rapid
development and wide application of LLMs, challenges such as ethical alignment,
controllability, and predictability of these models have become important
research topics. This study investigates an innovative simulation-based
multi-agent system within a virtual reality framework that replicates the
real-world environment. The framework is populated by automated 'digital
citizens,' simulating complex social structures and interactions to examine and
optimize AGI. Application of various theories from the fields of sociology,
social psychology, computer science, physics, biology, and economics
demonstrates the possibility of a more human-aligned and socially responsible
AGI. The purpose of such a digital environment is to provide a dynamic platform
where advanced AI agents can interact and make independent decisions, thereby
mimicking realistic scenarios. The actors in this digital city, operated by the
LLMs, serve as the primary agents, exhibiting high degrees of autonomy. While
this approach shows immense potential, there are notable challenges and
limitations, most significantly the unpredictable nature of real-world social
dynamics. This research endeavors to contribute to the development and
refinement of AGI, emphasizing the integration of social, ethical, and
theoretical dimensions for future research.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning
Abstract: In the realm of few-shot learning, foundation models like CLIP have proven
effective but exhibit limitations in cross-domain robustness especially in
few-shot settings. Recent works add text as an extra modality to enhance the
performance of these models. Most of these approaches treat text as an
auxiliary modality without fully exploring its potential to elucidate the
underlying class visual features distribution. In this paper, we present a
novel approach that leverages text-derived statistics to predict the mean and
covariance of the visual feature distribution for each class. This predictive
framework enriches the latent space, yielding more robust and generalizable
few-shot learning models. We demonstrate the efficacy of incorporating both
mean and covariance statistics in improving few-shot classification performance
across various datasets. Our method shows that we can use text to predict the
mean and covariance of the distribution offering promising improvements in
few-shot learning scenarios.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Visual Explanations via Iterated Integrated Attributions
Abstract: We introduce Iterated Integrated Attributions (IIA) - a generic method for
explaining the predictions of vision models. IIA employs iterative integration
across the input image, the internal representations generated by the model,
and their gradients, yielding precise and focused explanation maps. We
demonstrate the effectiveness of IIA through comprehensive evaluations across
various tasks, datasets, and network architectures. Our results showcase that
IIA produces accurate explanation maps, outperforming other state-of-the-art
explanation techniques.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Robust Fine-Tuning of Vision-Language Models for Domain Generalization
Abstract: Transfer learning enables the sharing of common knowledge among models for a
variety of downstream tasks, but traditional methods suffer in limited training
data settings and produce narrow models incapable of effectively generalizing
under distribution shifts. Foundation models have recently demonstrated
impressive zero-shot inference capabilities and robustness under distribution
shifts. However, zero-shot evaluation for these models has been predominantly
confined to benchmarks with simple distribution shifts, limiting our
understanding of their effectiveness under the more realistic shifts found in
practice. Moreover, common fine-tuning methods for these models have yet to be
evaluated against vision models in few-shot scenarios where training data is
limited. To address these gaps, we present a new recipe for few-shot
fine-tuning of the popular vision-language foundation model CLIP and evaluate
its performance on challenging benchmark datasets with realistic distribution
shifts from the WILDS collection. Our experimentation demonstrates that, while
zero-shot CLIP fails to match performance of trained vision models on more
complex benchmarks, few-shot CLIP fine-tuning outperforms its vision-only
counterparts in terms of in-distribution and out-of-distribution accuracy at
all levels of training data availability. This provides a strong incentive for
adoption of foundation models within few-shot learning applications operating
with real-world data. Code is available at
https://github.com/mit-ll/robust-vision-language-finetuning

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects
Abstract: Automatic assessment of impairment and disease severity is a key challenge in
data-driven medicine. We propose a novel framework to address this challenge,
which leverages AI models trained exclusively on healthy individuals. The
COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the
decrease in confidence of these models when presented with impaired or diseased
patients to quantify their deviation from the healthy population. We applied
the COBRA score to address a key limitation of current clinical evaluation of
upper-body impairment in stroke patients. The gold-standard Fugl-Meyer
Assessment (FMA) requires in-person administration by a trained assessor for
30-45 minutes, which restricts monitoring frequency and precludes physicians
from adapting rehabilitation protocols to the progress of each patient. The
COBRA score, computed automatically in under one minute, is shown to be
strongly correlated with the FMA on an independent test cohort for two
different data modalities: wearable sensors ($\rho = 0.845$, 95% CI
[0.743,0.908]) and video ($\rho = 0.746$, 95% C.I [0.594, 0.847]). To
demonstrate the generalizability of the approach to other conditions, the COBRA
score was also applied to quantify severity of knee osteoarthritis from
magnetic-resonance imaging scans, again achieving significant correlation with
an independent clinical assessment ($\rho = 0.644$, 95% C.I [0.585,0.696]).

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Optimizing IaC Configurations: a Case Study Using Nature-inspired Computing
Abstract: In the last years, one of the fields of artificial intelligence that has been
investigated the most is nature-inspired computing. The research done on this
specific topic showcases the interest that sparks in researchers and
practitioners, who put their focus on this paradigm because of the adaptability
and ability of nature-inspired algorithms to reach high-quality outcomes on a
wide range of problems. In fact, this kind of methods has been successfully
applied to solve real-world problems in heterogeneous fields such as medicine,
transportation, industry, or software engineering. Our main objective with this
paper is to describe a tool based on nature-inspired computing for solving a
specific software engineering problem. The problem faced consists of optimizing
Infrastructure as Code deployment configurations. For this reason, the name of
the system is IaC Optimizer Platform. A prototypical version of the IOP was
described in previous works, in which the functionality of this platform was
introduced. With this paper, we take a step forward by describing the final
release of the IOP, highlighting its main contribution regarding the current
state-of-the-art, and justifying the decisions made on its implementation.
Also, we contextualize the IOP within the complete platform in which it is
embedded, describing how a user can benefit from its use. To do that, we also
present and solve a real-world use case.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation
Abstract: In Conversational Recommendation System (CRS), an agent is asked to recommend
a set of items to users within natural language conversations. To address the
need for both conversational capability and personalized recommendations, prior
works have utilized separate recommendation and dialogue modules. However, such
approach inevitably results in a discrepancy between recommendation results and
generated responses. To bridge the gap, we propose a multi-task learning for a
unified CRS, where a single model jointly learns both tasks via Contextualized
Knowledge Distillation (ConKD). We introduce two versions of ConKD: hard gate
and soft gate. The former selectively gates between two task-specific teachers,
while the latter integrates knowledge from both teachers. Our gates are
computed on-the-fly in a context-specific manner, facilitating flexible
integration of relevant knowledge. Extensive experiments demonstrate that our
single model significantly improves recommendation performance while enhancing
fluency, and achieves comparable results in terms of diversity.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: HeTriNet: Heterogeneous Graph Triplet Attention Network for Drug-Target-Disease Interaction
Abstract: Modeling the interactions between drugs, targets, and diseases is paramount
in drug discovery and has significant implications for precision medicine and
personalized treatments. Current approaches frequently consider drug-target or
drug-disease interactions individually, ignoring the interdependencies among
all three entities. Within human metabolic systems, drugs interact with protein
targets in cells, influencing target activities and subsequently impacting
biological pathways to promote healthy functions and treat diseases. Moving
beyond binary relationships and exploring tighter triple relationships is
essential to understanding drugs' mechanism of action (MoAs). Moreover,
identifying the heterogeneity of drugs, targets, and diseases, along with their
distinct characteristics, is critical to model these complex interactions
appropriately. To address these challenges, we effectively model the
interconnectedness of all entities in a heterogeneous graph and develop a novel
Heterogeneous Graph Triplet Attention Network (\texttt{HeTriNet}).
\texttt{HeTriNet} introduces a novel triplet attention mechanism within this
heterogeneous graph structure. Beyond pairwise attention as the importance of
an entity for the other one, we define triplet attention to model the
importance of pairs for entities in the drug-target-disease triplet prediction
problem. Experimental results on real-world datasets show that
\texttt{HeTriNet} outperforms several baselines, demonstrating its remarkable
proficiency in uncovering novel drug-target-disease relationships.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Classification of Tabular Data by Text Processing
Abstract: Natural Language Processing technology has advanced vastly in the past
decade. Text processing has been successfully applied to a wide variety of
domains. In this paper, we propose a novel framework, Text Based
Classification(TBC), that uses state of the art text processing techniques to
solve classification tasks on tabular data. We provide a set of controlled
experiments where we present the benefits of using this approach against other
classification methods. Experimental results on several data sets also show
that this framework achieves comparable performance to that of several state of
the art models in accuracy, precision and recall of predicted classes.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection
Abstract: Automating the assembly of objects from their parts is a complex problem with
innumerable applications in manufacturing, maintenance, and recycling. Unlike
existing research, which is limited to target segmentation, pose regression, or
using fixed target blueprints, our work presents a holistic multi-level
framework for part assembly planning consisting of part assembly sequence
inference, part motion planning, and robot contact optimization. We present the
Part Assembly Sequence Transformer (PAST) -- a sequence-to-sequence neural
network -- to infer assembly sequences recursively from a target blueprint. We
then use a motion planner and optimization to generate part movements and
contacts. To train PAST, we introduce D4PAS: a large-scale Dataset for Part
Assembly Sequences (D4PAS) consisting of physically valid sequences for
industrial objects. Experimental results show that our approach generalizes
better than prior methods while needing significantly less computational time
for inference.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Dialogue-based generation of self-driving simulation scenarios using Large Language Models
Abstract: Simulation is an invaluable tool for developing and evaluating controllers
for self-driving cars. Current simulation frameworks are driven by
highly-specialist domain specific languages, and so a natural language
interface would greatly enhance usability. But there is often a gap, consisting
of tacit assumptions the user is making, between a concise English utterance
and the executable code that captures the user's intent. In this paper we
describe a system that addresses this issue by supporting an extended
multimodal interaction: the user can follow up prior instructions with
refinements or revisions, in reaction to the simulations that have been
generated from their utterances so far. We use Large Language Models (LLMs) to
map the user's English utterances in this interaction into domain-specific
code, and so we explore the extent to which LLMs capture the context
sensitivity that's necessary for computing the speaker's intended message in
discourse.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Online Boosting Adaptive Learning under Concept Drift for Multistream Classification
Abstract: Multistream classification poses significant challenges due to the necessity
for rapid adaptation in dynamic streaming processes with concept drift. Despite
the growing research outcomes in this area, there has been a notable oversight
regarding the temporal dynamic relationships between these streams, leading to
the issue of negative transfer arising from irrelevant data. In this paper, we
propose a novel Online Boosting Adaptive Learning (OBAL) method that
effectively addresses this limitation by adaptively learning the dynamic
correlation among different streams. Specifically, OBAL operates in a
dual-phase mechanism, in the first of which we design an Adaptive COvariate
Shift Adaptation (AdaCOSA) algorithm to construct an initialized ensemble model
using archived data from various source streams, thus mitigating the covariate
shift while learning the dynamic correlations via an adaptive re-weighting
strategy. During the online process, we employ a Gaussian Mixture Model-based
weighting mechanism, which is seamlessly integrated with the acquired
correlations via AdaCOSA to effectively handle asynchronous drift. This
approach significantly improves the predictive performance and stability of the
target stream. We conduct comprehensive experiments on several synthetic and
real-world data streams, encompassing various drifting scenarios and types. The
results clearly demonstrate that OBAL achieves remarkable advancements in
addressing multistream classification problems by effectively leveraging
positive knowledge derived from multiple sources.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: From Dialogue to Diagram: Task and Relationship Extraction from Natural Language for Accelerated Business Process Prototyping
Abstract: The automatic transformation of verbose, natural language descriptions into
structured process models remains a challenge of significant complexity - This
paper introduces a contemporary solution, where central to our approach, is the
use of dependency parsing and Named Entity Recognition (NER) for extracting key
elements from textual descriptions. Additionally, we utilize
Subject-Verb-Object (SVO) constructs for identifying action relationships and
integrate semantic analysis tools, including WordNet, for enriched contextual
understanding. A novel aspect of our system is the application of neural
coreference resolution, integrated with the SpaCy framework, enhancing the
precision of entity linkage and anaphoric references. Furthermore, the system
adeptly handles data transformation and visualization, converting extracted
information into BPMN (Business Process Model and Notation) diagrams. This
methodology not only streamlines the process of capturing and representing
business workflows but also significantly reduces the manual effort and
potential for error inherent in traditional modeling approaches.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code
Abstract: In this work we systematically review the recent advancements in code
processing with language models, covering 50+ models, 30+ evaluation tasks,
170+ datasets, and 700 related works. We break down code processing models into
general language models represented by the GPT family and specialized models
that are specifically pretrained on code, often with tailored objectives. We
discuss the relations and differences between these models, and highlight the
historical transition of code modeling from statistical models and RNNs to
pretrained Transformers and LLMs, which is exactly the same course that had
been taken by NLP. We also discuss code-specific features such as AST, CFG, and
unit tests, along with their application in training code language models, and
identify key challenges and potential future directions in this domain. We keep
the survey open and updated on GitHub at
https://github.com/codefuse-ai/Awesome-Code-LLM.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Mixing-Denoising Generalizable Occupancy Networks
Abstract: While current state-of-the-art generalizable implicit neural shape models
rely on the inductive bias of convolutions, it is still not entirely clear how
properties emerging from such biases are compatible with the task of 3D
reconstruction from point cloud. We explore an alternative approach to
generalizability in this context. We relax the intrinsic model bias (i.e. using
MLPs to encode local features as opposed to convolutions) and constrain the
hypothesis space instead with an auxiliary regularization related to the
reconstruction task, i.e. denoising. The resulting model is the first only-MLP
locally conditioned implicit shape reconstruction from point cloud network with
fast feed forward inference. Point cloud borne features and denoising offsets
are predicted from an exclusively MLP-made network in a single forward pass. A
decoder predicts occupancy probabilities for queries anywhere in space by
pooling nearby features from the point cloud order-invariantly, guided by
denoised relative positional encoding. We outperform the state-of-the-art
convolutional method while using half the number of model parameters.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Prompted Zero-Shot Multi-label Classification of Factual Incorrectness in Machine-Generated Summaries
Abstract: This study addresses the critical issue of factual inaccuracies in
machine-generated text summaries, an increasingly prevalent issue in
information dissemination. Recognizing the potential of such errors to
compromise information reliability, we investigate the nature of factual
inconsistencies across machine-summarized content. We introduce a prompt-based
classification system that categorizes errors into four distinct types:
misrepresentation, inaccurate quantities or measurements, false attribution,
and fabrication. The participants are tasked with evaluating a corpus of
machine-generated summaries against their original articles. Our methodology
employs qualitative judgements to identify the occurrence of factual
distortions. The results show that our prompt-based approaches are able to
detect the type of errors in the summaries to some extent, although there is
scope for improvement in our classification systems.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Unsupervised textile defect detection using convolutional neural networks
Abstract: In this study, we propose a novel motif-based approach for unsupervised
textile anomaly detection that combines the benefits of traditional
convolutional neural networks with those of an unsupervised learning paradigm.
It consists of five main steps: preprocessing, automatic pattern period
extraction, patch extraction, features selection and anomaly detection. This
proposed approach uses a new dynamic and heuristic method for feature selection
which avoids the drawbacks of initialization of the number of filters (neurons)
and their weights, and those of the backpropagation mechanism such as the
vanishing gradients, which are common practice in the state-of-the-art methods.
The design and training of the network are performed in a dynamic and input
domain-based manner and, thus, no ad-hoc configurations are required. Before
building the model, only the number of layers and the stride are defined. We do
not initialize the weights randomly nor do we define the filter size or number
of filters as conventionally done in CNN-based approaches. This reduces effort
and time spent on hyperparameter initialization and fine-tuning. Only one
defect-free sample is required for training and no further labeled data is
needed. The trained network is then used to detect anomalies on defective
fabric samples. We demonstrate the effectiveness of our approach on the
Patterned Fabrics benchmark dataset. Our algorithm yields reliable and
competitive results (on recall, precision, accuracy and f1- measure) compared
to state-of-the-art unsupervised approaches, in less time, with efficient
training in a single epoch and a lower computational cost.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Measuring Five Accountable Talk Moves to Improve Instruction at Scale
Abstract: Providing consistent, individualized feedback to teachers on their
instruction can improve student learning outcomes. Such feedback can especially
benefit novice instructors who teach on online platforms and have limited
access to instructional training. To build scalable measures of instruction, we
fine-tune RoBERTa and GPT models to identify five instructional talk moves
inspired by accountable talk theory: adding on, connecting, eliciting, probing
and revoicing students' ideas. We fine-tune these models on a newly annotated
dataset of 2500 instructor utterances derived from transcripts of small group
instruction in an online computer science course, Code in Place. Although we
find that GPT-3 consistently outperforms RoBERTa in terms of precision, its
recall varies significantly. We correlate the instructors' use of each talk
move with indicators of student engagement and satisfaction, including
students' section attendance, section ratings, and assignment completion rates.
We find that using talk moves generally correlates positively with student
outcomes, and connecting student ideas has the largest positive impact. These
results corroborate previous research on the effectiveness of accountable talk
moves and provide exciting avenues for using these models to provide
instructors with useful, scalable feedback.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph
Abstract: Heterogeneous graph neural networks have become popular in various domains.
However, their generalizability and interpretability are limited due to the
discrepancy between their inherent inference flows and human reasoning logic or
underlying causal relationships for the learning problem. This study introduces
a novel solution, HG-SCM (Heterogeneous Graph as Structural Causal Model). It
can mimic the human perception and decision process through two key steps:
constructing intelligible variables based on semantics derived from the graph
schema and automatically learning task-level causal relationships among these
variables by incorporating advanced causal discovery techniques. We compared
HG-SCM to seven state-of-the-art baseline models on three real-world datasets,
under three distinct and ubiquitous out-of-distribution settings. HG-SCM
achieved the highest average performance rank with minimal standard deviation,
substantiating its effectiveness and superiority in terms of both predictive
power and generalizability. Additionally, the visualization and analysis of the
auto-learned causal diagrams for the three tasks aligned well with domain
knowledge and human cognition, demonstrating prominent interpretability.
HG-SCM's human-like nature and its enhanced generalizability and
interpretability make it a promising solution for special scenarios where
transparency and trustworthiness are paramount.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion
Abstract: Distributional reinforcement learning algorithms have attempted to utilize
estimated uncertainty for exploration, such as optimism in the face of
uncertainty. However, using the estimated variance for optimistic exploration
may cause biased data collection and hinder convergence or performance. In this
paper, we present a novel distributional reinforcement learning algorithm that
selects actions by randomizing risk criterion to avoid one-sided tendency on
risk. We provide a perturbed distributional Bellman optimality operator by
distorting the risk measure and prove the convergence and optimality of the
proposed method with the weaker contraction property. Our theoretical results
support that the proposed method does not fall into biased exploration and is
guaranteed to converge to an optimal return. Finally, we empirically show that
our method outperforms other existing distribution-based algorithms in various
environments including Atari 55 games.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: ADAPTER-RL: Adaptation of Any Agent using Reinforcement Learning
Abstract: Deep Reinforcement Learning (DRL) agents frequently face challenges in
adapting to tasks outside their training distribution, including issues with
over-fitting, catastrophic forgetting and sample inefficiency. Although the
application of adapters has proven effective in supervised learning contexts
such as natural language processing and computer vision, their potential within
the DRL domain remains largely unexplored. This paper delves into the
integration of adapters in reinforcement learning, presenting an innovative
adaptation strategy that demonstrates enhanced training efficiency and
improvement of the base-agent, experimentally in the nanoRTS environment, a
real-time strategy (RTS) game simulation. Our proposed universal approach is
not only compatible with pre-trained neural networks but also with rule-based
agents, offering a means to integrate human expertise.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Batch Bayesian Optimization for Replicable Experimental Design
Abstract: Many real-world experimental design problems (a) evaluate multiple
experimental conditions in parallel and (b) replicate each condition multiple
times due to large and heteroscedastic observation noise. Given a fixed total
budget, this naturally induces a trade-off between evaluating more unique
conditions while replicating each of them fewer times vs. evaluating fewer
unique conditions and replicating each more times. Moreover, in these problems,
practitioners may be risk-averse and hence prefer an input with both good
average performance and small variability. To tackle both challenges, we
propose the Batch Thompson Sampling for Replicable Experimental Design
(BTS-RED) framework, which encompasses three algorithms. Our BTS-RED-Known and
BTS-RED-Unknown algorithms, for, respectively, known and unknown noise
variance, choose the number of replications adaptively rather than
deterministically such that an input with a larger noise variance is replicated
more times. As a result, despite the noise heteroscedasticity, both algorithms
enjoy a theoretical guarantee and are asymptotically no-regret. Our
Mean-Var-BTS-RED algorithm aims at risk-averse optimization and is also
asymptotically no-regret. We also show the effectiveness of our algorithms in
two practical real-world applications: precision agriculture and AutoML.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: FlowZero: Zero-Shot Text-to-Video Synthesis with LLM-Driven Dynamic Scene Syntax
Abstract: Text-to-video (T2V) generation is a rapidly growing research area that aims
to translate the scenes, objects, and actions within complex video text into a
sequence of coherent visual frames. We present FlowZero, a novel framework that
combines Large Language Models (LLMs) with image diffusion models to generate
temporally-coherent videos. FlowZero uses LLMs to understand complex
spatio-temporal dynamics from text, where LLMs can generate a comprehensive
dynamic scene syntax (DSS) containing scene descriptions, object layouts, and
background motion patterns. These elements in DSS are then used to guide the
image diffusion model for video generation with smooth object motions and
frame-to-frame coherence. Moreover, FlowZero incorporates an iterative
self-refinement process, enhancing the alignment between the spatio-temporal
layouts and the textual prompts for the videos. To enhance global coherence, we
propose enriching the initial noise of each frame with motion dynamics to
control the background movement and camera motion adaptively. By using
spatio-temporal syntaxes to guide the diffusion process, FlowZero achieves
improvement in zero-shot video synthesis, generating coherent videos with vivid
motion.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs
Abstract: Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve
semi-supervised node classification on graphs, by training a student MLP by
knowledge distillation (KD) from a teacher graph neural network (GNN). While
previous studies have focused mostly on training the student MLP by matching
the output probability distributions between the teacher and student models
during KD, it has not been systematically studied how to inject the structural
information in an explicit and interpretable manner. Inspired by GNNs that
separate feature transformation $T$ and propagation $\Pi$, we re-frame the KD
process as enabling the student MLP to explicitly learn both $T$ and $\Pi$.
Although this can be achieved by applying the inverse propagation $\Pi^{-1}$
before distillation from the teacher GNN, it still comes with a high
computational cost from large matrix multiplications during training. To solve
this problem, we propose Propagate & Distill (P&D), which propagates the output
of the teacher GNN before KD and can be interpreted as an approximate process
of the inverse propagation $\Pi^{-1}$. Through comprehensive evaluations using
real-world benchmark datasets, we demonstrate the effectiveness of P&D by
showing further performance boost of the student MLP.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Prompting LLMs with content plans to enhance the summarization of scientific articles
Abstract: This paper presents novel prompting techniques to improve the performance of
automatic summarization systems for scientific articles. Scientific article
summarization is highly challenging due to the length and complexity of these
documents. We conceive, implement, and evaluate prompting techniques that
provide additional contextual information to guide summarization systems.
Specifically, we feed summarizers with lists of key terms extracted from
articles, such as author keywords or automatically generated keywords. Our
techniques are tested with various summarization models and input texts.
Results show performance gains, especially for smaller models summarizing
sections separately. This evidences that prompting is a promising approach to
overcoming the limitations of less powerful systems. Our findings introduce a
new research direction of using prompts to aid smaller models.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Perturbation-based Active Learning for Question Answering
Abstract: Building a question answering (QA) model with less annotation costs can be
achieved by utilizing active learning (AL) training strategy. It selects the
most informative unlabeled training data to update the model effectively.
Acquisition functions for AL are used to determine how informative each
training example is, such as uncertainty or diversity based sampling. In this
work, we propose a perturbation-based active learning acquisition strategy and
demonstrate it is more effective than existing commonly used strategies.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Unsupervised Behavior Extraction via Random Intent Priors
Abstract: Reward-free data is abundant and contains rich prior knowledge of human
behaviors, but it is not well exploited by offline reinforcement learning (RL)
algorithms. In this paper, we propose UBER, an unsupervised approach to extract
useful behaviors from offline reward-free datasets via diversified rewards.
UBER assigns different pseudo-rewards sampled from a given prior distribution
to different agents to extract a diverse set of behaviors, and reuse them as
candidate policies to facilitate the learning of new tasks. Perhaps
surprisingly, we show that rewards generated from random neural networks are
sufficient to extract diverse and useful behaviors, some even close to expert
ones. We provide both empirical and theoretical evidence to justify the use of
random priors for the reward function. Experiments on multiple benchmarks
showcase UBER's ability to learn effective and diverse behavior sets that
enhance sample efficiency for online RL, outperforming existing baselines. By
reducing reliance on human supervision, UBER broadens the applicability of RL
to real-world scenarios with abundant reward-free data.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
Abstract: This paper studies causal representation learning, the task of recovering
high-level latent variables and their causal relationships from low-level data
that we observe, assuming access to observations generated from multiple
environments. While existing works are able to prove full identifiability of
the underlying data generating process, they typically assume access to
single-node, hard interventions which is rather unrealistic in practice. The
main contribution of this paper is characterize a notion of identifiability
which is provably the best one can achieve when hard interventions are not
available. First, for linear causal models, we provide identifiability
guarantee for data observed from general environments without assuming any
similarities between them. While the causal graph is shown to be fully
recovered, the latent variables are only identified up to an effect-domination
ambiguity (EDA). We then propose an algorithm, LiNGCReL which is guaranteed to
recover the ground-truth model up to EDA, and we demonstrate its effectiveness
via numerical experiments. Moving on to general non-parametric causal models,
we prove the same idenfifiability guarantee assuming access to groups of soft
interventions. Finally, we provide counterparts of our identifiability results,
indicating that EDA is basically inevitable in our setting.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Estimation of Concept Explanations Should be Uncertainty Aware
Abstract: Model explanations are very valuable for interpreting and debugging
prediction models. We study a specific kind of global explanations called
Concept Explanations, where the goal is to interpret a model using
human-understandable concepts. Recent advances in multi-modal learning
rekindled interest in concept explanations and led to several label-efficient
proposals for estimation. However, existing estimation methods are unstable to
the choice of concepts or dataset that is used for computing explanations. We
observe that instability in explanations is due to high variance in point
estimation of importance scores. We propose an uncertainty aware Bayesian
estimation method, which readily improved reliability of the concept
explanations. We demonstrate with theoretical analysis and empirical evaluation
that explanations computed by our method are more reliable while also being
label-efficient and faithful.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Robust and Scalable Hyperdimensional Computing With Brain-Like Neural Adaptations
Abstract: The Internet of Things (IoT) has facilitated many applications utilizing
edge-based machine learning (ML) methods to analyze locally collected data.
Unfortunately, popular ML algorithms often require intensive computations
beyond the capabilities of today's IoT devices. Brain-inspired hyperdimensional
computing (HDC) has been introduced to address this issue. However, existing
HDCs use static encoders, requiring extremely high dimensionality and hundreds
of training iterations to achieve reasonable accuracy. This results in a huge
efficiency loss, severely impeding the application of HDCs in IoT systems. We
observed that a main cause is that the encoding module of existing HDCs lacks
the capability to utilize and adapt to information learned during training. In
contrast, neurons in human brains dynamically regenerate all the time and
provide more useful functionalities when learning new information. While the
goal of HDC is to exploit the high-dimensionality of randomly generated base
hypervectors to represent the information as a pattern of neural activity, it
remains challenging for existing HDCs to support a similar behavior as brain
neural regeneration. In this work, we present dynamic HDC learning frameworks
that identify and regenerate undesired dimensions to provide adequate accuracy
with significantly lowered dimensionalities, thereby accelerating both the
training and inference.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection
Abstract: Graph Neural Networks (GNNs) have emerged as a powerful tool for
representation learning on graphs, but they often suffer from overfitting and
label noise issues, especially when the data is scarce or imbalanced. Different
from the paradigm of previous methods that rely on single-node confidence, in
this paper, we introduce a novel Class-wise Selection for Graph Neural
Networks, dubbed CSGNN, which employs a neighbor-aggregated latent space to
adaptively select reliable nodes across different classes. Specifically, 1) to
tackle the class imbalance issue, we introduce a dynamic class-wise selection
mechanism, leveraging the clustering technique to identify clean nodes based on
the neighbor-aggregated confidences. In this way, our approach can avoid the
pitfalls of biased sampling which is common with global threshold techniques.
2) To alleviate the problem of noisy labels, built on the concept of the
memorization effect, CSGNN prioritizes learning from clean nodes before noisy
ones, thereby iteratively enhancing model performance while mitigating label
noise. Through extensive experiments, we demonstrate that CSGNN outperforms
state-of-the-art methods in terms of both effectiveness and robustness.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Empowering Autonomous Driving with Large Language Models: A Safety Perspective
Abstract: Autonomous Driving (AD) faces crucial hurdles for commercial launch, notably
in the form of diminished public trust and safety concerns from long-tail
unforeseen driving scenarios. This predicament is due to the limitation of deep
neural networks in AD software, which struggle with interpretability and
exhibit poor generalization capabilities in out-of-distribution and uncertain
scenarios. To this end, this paper advocates for the integration of Large
Language Models (LLMs) into the AD system, leveraging their robust common-sense
knowledge, reasoning abilities, and human-interaction capabilities. The
proposed approach deploys the LLM as an intelligent decision-maker in planning,
incorporating safety verifiers for contextual safety learning to enhance
overall AD performance and safety. We present results from two case studies
that affirm the efficacy of our approach. We further discuss the potential
integration of LLM for other AD software components including perception,
prediction, and simulation. Despite the observed challenges in the case
studies, the integration of LLMs is promising and beneficial for reinforcing
both safety and performance in AD.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness
Abstract: We propose the task of Panoptic Scene Completion (PSC) which extends the
recently popular Semantic Scene Completion (SSC) task with instance-level
information to produce a richer understanding of the 3D scene. Our PSC proposal
utilizes a hybrid mask-based technique on the non-empty voxels from sparse
multi-scale completions. Whereas the SSC literature overlooks uncertainty which
is critical for robotics applications, we instead propose an efficient
ensembling to estimate both voxel-wise and instance-wise uncertainties along
PSC. This is achieved by building on a multi-input multi-output (MIMO)
strategy, while improving performance and yielding better uncertainty for
little additional compute. Additionally, we introduce a technique to aggregate
permutation-invariant mask predictions. Our experiments demonstrate that our
method surpasses all baselines in both Panoptic Scene Completion and
uncertainty estimation on three large-scale autonomous driving datasets. Our
code and data are available at https://astra-vision.github.io/PaSCo .

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Meta Learning for Multi-View Visuomotor Systems
Abstract: This paper introduces a new approach for quickly adapting a multi-view
visuomotor system for robots to varying camera configurations from the baseline
setup. It utilises meta-learning to fine-tune the perceptual network while
keeping the policy network fixed. Experimental results demonstrate a
significant reduction in the number of new training episodes needed to attain
baseline performance.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: rTisane: Externalizing conceptual models for data analysis increases engagement with domain knowledge and improves statistical model quality
Abstract: Statistical models should accurately reflect analysts' domain knowledge about
variables and their relationships. While recent tools let analysts express
these assumptions and use them to produce a resulting statistical model, it
remains unclear what analysts want to express and how externalization impacts
statistical model quality. This paper addresses these gaps. We first conduct an
exploratory study of analysts using a domain-specific language (DSL) to express
conceptual models. We observe a preference for detailing how variables relate
and a desire to allow, and then later resolve, ambiguity in their conceptual
models. We leverage these findings to develop rTisane, a DSL for expressing
conceptual models augmented with an interactive disambiguation process. In a
controlled evaluation, we find that rTisane's DSL helps analysts engage more
deeply with and accurately externalize their assumptions. rTisane also leads to
statistical models that match analysts' assumptions, maintain analysis intent,
and better fit the data.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models
Abstract: Unlike most reinforcement learning agents which require an unrealistic amount
of environment interactions to learn a new behaviour, humans excel at learning
quickly by merely observing and imitating others. This ability highly depends
on the fact that humans have a model of their own embodiment that allows them
to infer the most likely actions that led to the observed behaviour. In this
paper, we propose Action Inference by Maximising Evidence (AIME) to replicate
this behaviour using world models. AIME consists of two distinct phases. In the
first phase, the agent learns a world model from its past experience to
understand its own body by maximising the ELBO. While in the second phase, the
agent is given some observation-only demonstrations of an expert performing a
novel task and tries to imitate the expert's behaviour. AIME achieves this by
defining a policy as an inference model and maximising the evidence of the
demonstration under the policy and world model. Our method is "zero-shot" in
the sense that it does not require further training for the world model or
online interactions with the environment after given the demonstration. We
empirically validate the zero-shot imitation performance of our method on the
Walker and Cheetah embodiment of the DeepMind Control Suite and find it
outperforms the state-of-the-art baselines. Code is available at:
https://github.com/argmax-ai/aime.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation
Abstract: In optimal transport (OT), a Monge map is known as a mapping that transports
a source distribution to a target distribution in the most cost-efficient way.
Recently, multiple neural estimators for Monge maps have been developed and
applied in diverse unpaired domain translation tasks, e.g. in single-cell
biology and computer vision. However, the classic OT framework enforces mass
conservation, which makes it prone to outliers and limits its applicability in
real-world scenarios. The latter can be particularly harmful in OT domain
translation tasks, where the relative position of a sample within a
distribution is explicitly taken into account. While unbalanced OT tackles this
challenge in the discrete setting, its integration into neural Monge map
estimators has received limited attention. We propose a theoretically grounded
method to incorporate unbalancedness into any Monge map estimator. We improve
existing estimators to model cell trajectories over time and to predict
cellular responses to perturbations. Moreover, our approach seamlessly
integrates with the OT flow matching (OT-FM) framework. While we show that
OT-FM performs competitively in image translation, we further improve
performance by incorporating unbalancedness (UOT-FM), which better preserves
relevant features. We hence establish UOT-FM as a principled method for
unpaired image translation.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Synthetic Data Generation for Bridging Sim2Real Gap in a Production Environment
Abstract: Synthetic data is being used lately for training deep neural networks in
computer vision applications such as object detection, object segmentation and
6D object pose estimation. Domain randomization hereby plays an important role
in reducing the simulation to reality gap. However, this generalization might
not be effective in specialized domains like a production environment involving
complex assemblies. Either the individual parts, trained with synthetic images,
are integrated in much larger assemblies making them indistinguishable from
their counterparts and result in false positives or are partially occluded just
enough to give rise to false negatives. Domain knowledge is vital in these
cases and if conceived effectively while generating synthetic data, can show a
considerable improvement in bridging the simulation to reality gap. This paper
focuses on synthetic data generation procedures for parts and assemblies used
in a production environment. The basic procedures for synthetic data generation
and their various combinations are evaluated and compared on images captured in
a production environment, where results show up to 15% improvement using
combinations of basic procedures. Reducing the simulation to reality gap in
this way can aid to utilize the true potential of robot assisted production
using artificial intelligence.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Using State-of-the-Art Speech Models to Evaluate Oral Reading Fluency in Ghana
Abstract: This paper reports on a set of three recent experiments utilizing large-scale
speech models to evaluate the oral reading fluency (ORF) of students in Ghana.
While ORF is a well-established measure of foundational literacy, assessing it
typically requires one-on-one sessions between a student and a trained
evaluator, a process that is time-consuming and costly. Automating the
evaluation of ORF could support better literacy instruction, particularly in
education contexts where formative assessment is uncommon due to large class
sizes and limited resources. To our knowledge, this research is among the first
to examine the use of the most recent versions of large-scale speech models
(Whisper V2 wav2vec2.0) for ORF assessment in the Global South.
  We find that Whisper V2 produces transcriptions of Ghanaian students reading
aloud with a Word Error Rate of 13.5. This is close to the model's average WER
on adult speech (12.8) and would have been considered state-of-the-art for
children's speech transcription only a few years ago. We also find that when
these transcriptions are used to produce fully automated ORF scores, they
closely align with scores generated by expert human graders, with a correlation
coefficient of 0.96. Importantly, these results were achieved on a
representative dataset (i.e., students with regional accents, recordings taken
in actual classrooms), using a free and publicly available speech model out of
the box (i.e., no fine-tuning). This suggests that using large-scale speech
models to assess ORF may be feasible to implement and scale in lower-resource,
linguistically diverse educational contexts.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: From Principle to Practice: Vertical Data Minimization for Machine Learning
Abstract: Aiming to train and deploy predictive models, organizations collect large
amounts of detailed client data, risking the exposure of private information in
the event of a breach. To mitigate this, policymakers increasingly demand
compliance with the data minimization (DM) principle, restricting data
collection to only that data which is relevant and necessary for the task.
Despite regulatory pressure, the problem of deploying machine learning models
that obey DM has so far received little attention. In this work, we address
this challenge in a comprehensive manner. We propose a novel vertical DM (vDM)
workflow based on data generalization, which by design ensures that no
full-resolution client data is collected during training and deployment of
models, benefiting client privacy by reducing the attack surface in case of a
breach. We formalize and study the corresponding problem of finding
generalizations that both maximize data utility and minimize empirical privacy
risk, which we quantify by introducing a diverse set of policy-aligned
adversarial scenarios. Finally, we propose a range of baseline vDM algorithms,
as well as Privacy-aware Tree (PAT), an especially effective vDM algorithm that
outperforms all baselines across several settings. We plan to release our code
as a publicly available library, helping advance the standardization of DM for
machine learning. Overall, we believe our work can help lay the foundation for
further exploration and adoption of DM principles in real-world applications.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model
Abstract: We introduce X-Adapter, a universal upgrader to enable the pretrained
plug-and-play modules (e.g., ControlNet, LoRA) to work directly with the
upgraded text-to-image diffusion model (e.g., SDXL) without further retraining.
We achieve this goal by training an additional network to control the frozen
upgraded model with the new text-image data pairs. In detail, X-Adapter keeps a
frozen copy of the old model to preserve the connectors of different plugins.
Additionally, X-Adapter adds trainable mapping layers that bridge the decoders
from models of different versions for feature remapping. The remapped features
will be used as guidance for the upgraded model. To enhance the guidance
ability of X-Adapter, we employ a null-text training strategy for the upgraded
model. After training, we also introduce a two-stage denoising strategy to
align the initial latents of X-Adapter and the upgraded model. Thanks to our
strategies, X-Adapter demonstrates universal compatibility with various plugins
and also enables plugins of different versions to work together, thereby
expanding the functionalities of diffusion community. To verify the
effectiveness of the proposed method, we conduct extensive experiments and the
results show that X-Adapter may facilitate wider application in the upgraded
foundational diffusion model.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Handshape recognition for Argentinian Sign Language using ProbSom
Abstract: Automatic sign language recognition is an important topic within the areas of
human-computer interaction and machine learning. On the one hand, it poses a
complex challenge that requires the intervention of various knowledge areas,
such as video processing, image processing, intelligent systems and
linguistics. On the other hand, robust recognition of sign language could
assist in the translation process and the integration of hearing-impaired
people.
  This paper offers two main contributions: first, the creation of a database
of handshapes for the Argentinian Sign Language (LSA), which is a topic that
has barely been discussed so far. Secondly, a technique for image processing,
descriptor extraction and subsequent handshape classification using a
supervised adaptation of self-organizing maps that is called ProbSom. This
technique is compared to others in the state of the art, such as Support Vector
Machines (SVM), Random Forests, and Neural Networks.
  The database that was built contains 800 images with 16 LSA handshapes, and
is a first step towards building a comprehensive database of Argentinian signs.
The ProbSom-based neural classifier, using the proposed descriptor, achieved an
accuracy rate above 90%.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Multimodality of AI for Education: Towards Artificial General Intelligence
Abstract: This paper presents a comprehensive examination of how multimodal artificial
intelligence (AI) approaches are paving the way towards the realization of
Artificial General Intelligence (AGI) in educational contexts. It scrutinizes
the evolution and integration of AI in educational systems, emphasizing the
crucial role of multimodality, which encompasses auditory, visual, kinesthetic,
and linguistic modes of learning. This research delves deeply into the key
facets of AGI, including cognitive frameworks, advanced knowledge
representation, adaptive learning mechanisms, strategic planning, sophisticated
language processing, and the integration of diverse multimodal data sources. It
critically assesses AGI's transformative potential in reshaping educational
paradigms, focusing on enhancing teaching and learning effectiveness, filling
gaps in existing methodologies, and addressing ethical considerations and
responsible usage of AGI in educational settings. The paper also discusses the
implications of multimodal AI's role in education, offering insights into
future directions and challenges in AGI development. This exploration aims to
provide a nuanced understanding of the intersection between AI, multimodality,
and education, setting a foundation for future research and development in AGI.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: TEAL: Tokenize and Embed ALL for Multi-modal Large Language Models
Abstract: Despite Multi-modal Large Language Models (MM-LLMs) have made exciting
strides recently, they are still struggling to efficiently model the
interactions among multi-modal inputs and the generation in non-textual
modalities. In this work, we propose TEAL (Tokenize and Embed ALl)}, an
approach to treat the input from any modality as a token sequence and learn a
joint embedding space for all modalities. Specifically, for the input from any
modality, TEAL first discretizes it into a token sequence with the
off-the-shelf tokenizer and embeds the token sequence into a joint embedding
space with a learnable embedding matrix. MM-LLMs just need to predict the
multi-modal tokens autoregressively as the textual LLMs do. Finally, the
corresponding de-tokenizer is applied to generate the output in each modality
based on the predicted token sequence. With the joint embedding space, TEAL
enables the frozen LLMs to perform both understanding and generation tasks
involving non-textual modalities, such as image and audio. Thus, the textual
LLM can just work as an interface and maintain its high performance in textual
understanding and generation. Experiments show that TEAL achieves substantial
improvements in multi-modal understanding, and implements a simple scheme for
multi-modal generations.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Kattis vs. ChatGPT: Assessment and Evaluation of Programming Tasks in the Age of Artificial Intelligence
Abstract: AI-powered education technologies can support students and teachers in
computer science education. However, with the recent developments in generative
AI, and especially the increasingly emerging popularity of ChatGPT, the
effectiveness of using large language models for solving programming tasks has
been underexplored. The present study examines ChatGPT's ability to generate
code solutions at different difficulty levels for introductory programming
courses. We conducted an experiment where ChatGPT was tested on 127 randomly
selected programming problems provided by Kattis, an automatic software grading
tool for computer science programs, often used in higher education. The results
showed that ChatGPT independently could solve 19 out of 127 programming tasks
generated and assessed by Kattis. Further, ChatGPT was found to be able to
generate accurate code solutions for simple problems but encountered
difficulties with more complex programming tasks. The results contribute to the
ongoing debate on the utility of AI-powered tools in programming education.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Pre-training LLMs using human-like development data corpus
Abstract: Pre-trained Large Language Models (LLMs) have shown success in a diverse set
of language inference and understanding tasks. The pre-training stage of LLMs
looks at a large corpus of raw textual data. The BabyLM shared task compares
LLM pre-training to human language acquisition, where the number of tokens seen
by 13-year-old kids is magnitudes smaller than the number of tokens seen by
LLMs. In this work, we pre-train and evaluate LLMs on their ability to learn
contextual word representations using roughly the same number of tokens as seen
by children. We provide a strong set of baselines; with different
architectures, evaluation of changes in performance across epochs, and reported
pre-training metrics for the strict small and strict tracks of the task. We
also try to loosely replicate the RoBERTa baseline given by the task organizers
to observe the training robustness to hyperparameter selection and
replicability. We provide the submission details to the strict and strict-small
tracks in this report.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Verb Conjugation in Transformers Is Determined by Linear Encodings of Subject Number
Abstract: Deep architectures such as Transformers are sometimes criticized for having
uninterpretable "black-box" representations. We use causal intervention
analysis to show that, in fact, some linguistic features are represented in a
linear, interpretable format. Specifically, we show that BERT's ability to
conjugate verbs relies on a linear encoding of subject number that can be
manipulated with predictable effects on conjugation accuracy. This encoding is
found in the subject position at the first layer and the verb position at the
last layer, but distributed across positions at middle layers, particularly
when there are multiple cues to subject number.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation
Abstract: Traditional 3D segmentation methods can only recognize a fixed range of
classes that appear in the training set, which limits their application in
real-world scenarios due to the lack of generalization ability. Large-scale
visual-language pre-trained models, such as CLIP, have shown their
generalization ability in the zero-shot 2D vision tasks, but are still unable
to be applied to 3D semantic segmentation directly. In this work, we focus on
zero-shot point cloud semantic segmentation and propose a simple yet effective
baseline to transfer the visual-linguistic knowledge implied in CLIP to point
cloud encoder at both feature and output levels. Both feature-level and
output-level alignments are conducted between 2D and 3D encoders for effective
knowledge transfer. Concretely, a Multi-granularity Cross-modal Feature
Alignment (MCFA) module is proposed to align 2D and 3D features from global
semantic and local position perspectives for feature-level alignment. For the
output level, per-pixel pseudo labels of unseen classes are extracted using the
pre-trained CLIP model as supervision for the 3D segmentation model to mimic
the behavior of the CLIP image encoder. Extensive experiments are conducted on
two popular benchmarks of point cloud segmentation. Our method outperforms
significantly previous state-of-the-art methods under zero-shot setting (+29.2%
mIoU on SemanticKITTI and 31.8% mIoU on nuScenes), and further achieves
promising results in the annotation-free point cloud semantic segmentation
setting, showing its great potential for label-efficient learning.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection
Abstract: Unsupervised graph anomaly detection is crucial for various practical
applications as it aims to identify anomalies in a graph that exhibit rare
patterns deviating significantly from the majority of nodes. Recent
advancements have utilized Graph Neural Networks (GNNs) to learn high-quality
node representations for anomaly detection by aggregating information from
neighborhoods. However, the presence of anomalies may render the observed
neighborhood unreliable and result in misleading information aggregation for
node representation learning. Selecting the proper neighborhood is critical for
graph anomaly detection but also challenging due to the absence of
anomaly-oriented guidance and the interdependence with representation learning.
To address these issues, we utilize the advantages of reinforcement learning in
adaptively learning in complex environments and propose a novel method that
incorporates Reinforcement neighborhood selection for unsupervised graph
ANomaly Detection (RAND). RAND begins by enriching the candidate neighbor pool
of the given central node with multiple types of indirect neighbors. Next, RAND
designs a tailored reinforcement anomaly evaluation module to assess the
reliability and reward of considering the given neighbor. Finally, RAND selects
the most reliable subset of neighbors based on these rewards and introduces an
anomaly-aware aggregator to amplify messages from reliable neighbors while
diminishing messages from unreliable ones. Extensive experiments on both three
synthetic and two real-world datasets demonstrate that RAND outperforms the
state-of-the-art methods.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations
Abstract: In this work, we address the challenge of multi-task image generation with
limited data for denoising diffusion probabilistic models (DDPM), a class of
generative models that produce high-quality images by reversing a noisy
diffusion process. We propose a novel method, SR-DDPM, that leverages
representation-based techniques from few-shot learning to effectively learn
from fewer samples across different tasks. Our method consists of a core meta
architecture with shared parameters, i.e., task-specific layers with exclusive
parameters. By exploiting the similarity between diverse data distributions,
our method can scale to multiple tasks without compromising the image quality.
We evaluate our method on standard image datasets and show that it outperforms
both unconditional and conditional DDPM in terms of FID and SSIM metrics.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: SCCA: Shifted Cross Chunk Attention for long contextual semantic expansion
Abstract: Sparse attention as a efficient method can significantly decrease the
computation cost, but current sparse attention tend to rely on window self
attention which block the global information flow. For this problem, we present
Shifted Cross Chunk Attention (SCCA), using different KV shifting strategy to
extend respective field in each attention layer. Except, we combine Dilated
Attention(DA) and Dilated Neighborhood Attention(DNA) to present Shifted
Dilated Attention(SDA). Both SCCA and SDA can accumulate attention results in
multi head attention to obtain approximate respective field in full attention.
In this paper, we conduct language modeling experiments using different pattern
of SCCA and combination of SCCA and SDA. The proposed shifted cross chunk
attention (SCCA) can effectively extend large language models (LLMs) to longer
context combined with Positional interpolation(PI) and LoRA than current sparse
attention. Notably, SCCA adopts LLaMA2 7B from 4k context to 8k in single V100.
This attention pattern can provide a Plug-and-play fine-tuning method to extend
model context while retaining their original architectures, and is compatible
with most existing techniques.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
Abstract: This book aims to provide an introduction to the topic of deep learning
algorithms. We review essential components of deep learning algorithms in full
mathematical detail including different artificial neural network (ANN)
architectures (such as fully-connected feedforward ANNs, convolutional ANNs,
recurrent ANNs, residual ANNs, and ANNs with batch normalization) and different
optimization algorithms (such as the basic stochastic gradient descent (SGD)
method, accelerated methods, and adaptive methods). We also cover several
theoretical aspects of deep learning algorithms such as approximation
capacities of ANNs (including a calculus for ANNs), optimization theory
(including Kurdyka-{\L}ojasiewicz inequalities), and generalization errors. In
the last part of the book some deep learning approximation methods for PDEs are
reviewed including physics-informed neural networks (PINNs) and deep Galerkin
methods. We hope that this book will be useful for students and scientists who
do not yet have any background in deep learning at all and would like to gain a
solid foundation as well as for practitioners who would like to obtain a firmer
mathematical understanding of the objects and methods considered in deep
learning.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: A Survey of the Various Methodologies Towards making Artificial Intelligence More Explainable
Abstract: Machines are being increasingly used in decision-making processes, resulting
in the realization that decisions need explanations. Unfortunately, an
increasing number of these deployed models are of a 'black-box' nature where
the reasoning behind the decisions is unknown. Hence, there is a need for
clarity behind the reasoning of these decisions. As humans, we would want these
decisions to be presented to us in an explainable manner. However, explanations
alone are insufficient. They do not necessarily tell us how to achieve an
outcome but merely tell us what achieves the given outcome. For this reason, my
research focuses on explainability/interpretability and how it extends to
counterfactual thinking.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Do Similar Entities have Similar Embeddings?
Abstract: Knowledge graph embedding models (KGEMs) developed for link prediction learn
vector representations for graph entities, known as embeddings. A common tacit
assumption is the KGE entity similarity assumption, which states that these
KGEMs retain the graph's structure within their embedding space, i.e., position
similar entities close to one another. This desirable property make KGEMs
widely used in downstream tasks such as recommender systems or drug
repurposing. Yet, the alignment of graph similarity with embedding space
similarity has rarely been formally evaluated. Typically, KGEMs are assessed
based on their sole link prediction capabilities, using ranked-based metrics
such as Hits@K or Mean Rank. This paper challenges the prevailing assumption
that entity similarity in the graph is inherently mirrored in the embedding
space. Therefore, we conduct extensive experiments to measure the capability of
KGEMs to cluster similar entities together, and investigate the nature of the
underlying factors. Moreover, we study if different KGEMs expose a different
notion of similarity. Datasets, pre-trained embeddings and code are available
at: https://github.com/nicolas-hbt/similar-embeddings.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Sparse Low-rank Adaptation of Pre-trained Language Models
Abstract: Fine-tuning pre-trained large language models in a parameter-efficient manner
is widely studied for its effectiveness and efficiency. The popular method of
low-rank adaptation (LoRA) offers a notable approach, hypothesizing that the
adaptation process is intrinsically low-dimensional. Although LoRA has
demonstrated commendable performance, it is implemented with a fixed and
unalterable intrinsic rank that might not always be the ideal choice.
Recognizing the need for more flexible adaptation, we extend the methodology of
LoRA to an innovative approach we call sparse low-rank adaptation (SoRA) that
enables dynamic adjustments to the intrinsic rank during the adaptation
process. We achieve this through the incorporation of a gate unit optimized
with proximal gradient method in the training stage, controlling the
cardinality of rank under the sparsity of the gate. In the subsequent inference
stage, we eliminate the parameter blocks corresponding to the zeroed-out ranks,
to reduce each SoRA module back to a concise yet rank-optimal LoRA. Our
approach strengthens the representation power of LoRA by initializing it with a
higher rank, while efficiently taming a temporarily increased number of
parameters via updating in a sparse way. We further introduce a sparsifying
scheduler for SoRA, aiming to examine the impact of the number of non-zero
parameters on the model's memorization and generalization. Our experimental
results demonstrate that SoRA can outperform other baselines even with 70%
retained parameters and 70% training time.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method
Abstract: Existing fashion recommendation systems encounter difficulties in using
visual data for accurate and personalized recommendations. This research
describes an innovative end-to-end pipeline that uses artificial intelligence
to provide fine-grained visual interpretation for fashion recommendations. When
customers upload images of desired products or outfits, the system
automatically generates meaningful descriptions emphasizing stylistic elements.
These captions guide retrieval from a global fashion product catalogue to offer
similar alternatives that fit the visual characteristics of the original image.
On a dataset of over 100,000 categorized fashion photos, the pipeline was
trained and evaluated. The F1-score for the object detection model was 0.97,
exhibiting exact fashion object recognition capabilities optimized for
recommendation. This visually aware system represents a key advancement in
customer engagement through personalized fashion recommendations

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Learning Independently from Causality in Multi-Agent Environments
Abstract: Multi-Agent Reinforcement Learning (MARL) comprises an area of growing
interest in the field of machine learning. Despite notable advances, there are
still problems that require investigation. The lazy agent pathology is a famous
problem in MARL that denotes the event when some of the agents in a MARL team
do not contribute to the common goal, letting the teammates do all the work. In
this work, we aim to investigate this problem from a causality-based
perspective. We intend to create the bridge between the fields of MARL and
causality and argue about the usefulness of this link. We study a fully
decentralised MARL setup where agents need to learn cooperation strategies and
show that there is a causal relation between individual observations and the
team reward. The experiments carried show how this relation can be used to
improve independent agents in MARL, resulting not only on better performances
as a team but also on the rise of more intelligent behaviours on individual
agents.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Taking control: Policies to address extinction risks from advanced AI
Abstract: This paper provides policy recommendations to reduce extinction risks from
advanced artificial intelligence (AI). First, we briefly provide background
information about extinction risks from AI. Second, we argue that voluntary
commitments from AI companies would be an inappropriate and insufficient
response. Third, we describe three policy proposals that would meaningfully
address the threats from advanced AI: (1) establishing a Multinational AGI
Consortium to enable democratic oversight of advanced AI (MAGIC), (2)
implementing a global cap on the amount of computing power used to train an AI
system (global compute cap), and (3) requiring affirmative safety evaluations
to ensure that risks are kept below acceptable levels (gating critical
experiments). MAGIC would be a secure, safety-focused, internationally-governed
institution responsible for reducing risks from advanced AI and performing
research to safely harness the benefits of AI. MAGIC would also maintain
emergency response infrastructure (kill switch) to swiftly halt AI development
or withdraw model deployment in the event of an AI-related emergency. The
global compute cap would end the corporate race toward dangerous AI systems
while enabling the vast majority of AI innovation to continue unimpeded. Gating
critical experiments would ensure that companies developing powerful AI systems
are required to present affirmative evidence that these models keep extinction
risks below an acceptable threshold. After describing these recommendations, we
propose intermediate steps that the international community could take to
implement these proposals and lay the groundwork for international coordination
around advanced AI.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: FoMo Rewards: Can we cast foundation models as reward functions?
Abstract: We explore the viability of casting foundation models as generic reward
functions for reinforcement learning. To this end, we propose a simple pipeline
that interfaces an off-the-shelf vision model with a large language model.
Specifically, given a trajectory of observations, we infer the likelihood of an
instruction describing the task that the user wants an agent to perform. We
show that this generic likelihood function exhibits the characteristics ideally
expected from a reward function: it associates high values with the desired
behaviour and lower values for several similar, but incorrect policies.
Overall, our work opens the possibility of designing open-ended agents for
interactive tasks via foundation models.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Investigating Multi-Pivot Ensembling with Massively Multilingual Machine Translation Models
Abstract: Massively multilingual machine translation models allow for the translation
of a large number of languages with a single model, but have limited
performance on low- and very-low-resource translation directions. Pivoting via
high-resource languages remains a strong strategy for low-resource directions,
and in this paper we revisit ways of pivoting through multiple languages.
Previous work has used a simple averaging of probability distributions from
multiple paths, but we find that this performs worse than using a single pivot,
and exacerbates the hallucination problem because the same hallucinations can
be probable across different paths. As an alternative, we propose MaxEns, a
combination strategy that is biased towards the most confident predictions,
hypothesising that confident predictions are less prone to be hallucinations.
We evaluate different strategies on the FLORES benchmark for 20 low-resource
language directions, demonstrating that MaxEns improves translation quality for
low-resource languages while reducing hallucination in translations, compared
to both direct translation and an averaging approach. On average, multi-pivot
strategies still lag behind using English as a single pivot language, raising
the question of how to identify the best pivoting strategy for a given
translation direction.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot
Abstract: The advent of tactile sensors in robotics has sparked many ideas on how
robots can leverage direct contact measurements of their environment
interactions to improve manipulation tasks. An important line of research in
this regard is that of grasp force control, which aims to manipulate objects
safely by limiting the amount of force exerted on the object. While prior works
have either hand-modeled their force controllers, employed model-based
approaches, or have not shown sim-to-real transfer, we propose a model-free
deep reinforcement learning approach trained in simulation and then transferred
to the robot without further fine-tuning. We therefore present a simulation
environment that produces realistic normal forces, which we use to train
continuous force control policies. An evaluation in which we compare against a
baseline and perform an ablation study shows that our approach outperforms the
hand-modeled baseline and that our proposed inductive bias and domain
randomization facilitate sim-to-real transfer. Code, models, and supplementary
videos are available on https://sites.google.com/view/rl-force-ctrl

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Speculative Exploration on the Concept of Artificial Agents Conducting Autonomous Research
Abstract: This paper engages in a speculative exploration of the concept of an
artificial agent capable of conducting research. Initially, it examines how the
act of research can be conceptually characterized, aiming to provide a starting
point for discussions about what it means to create such agents. The focus then
shifts to the core components of research: question formulation, hypothesis
generation, and hypothesis verification. This discussion includes a
consideration of the potential and challenges associated with enabling machines
to autonomously perform these tasks. Subsequently, this paper briefly considers
the overlapping themes and interconnections that underlie them. Finally, the
paper presents preliminary thoughts on prototyping as an initial step towards
uncovering the challenges involved in developing these research-capable agents.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Towards Context-Aware Domain Generalization: Representing Environments with Permutation-Invariant Networks
Abstract: In this work, we show that information about the context of an input $X$ can
improve the predictions of deep learning models when applied in new domains or
production environments. We formalize the notion of context as a
permutation-invariant representation of a set of data points that originate
from the same environment/domain as the input itself. These representations are
jointly learned with a standard supervised learning objective, providing
incremental information about the unknown outcome. Furthermore, we offer a
theoretical analysis of the conditions under which our approach can, in
principle, yield benefits, and formulate two necessary criteria that can be
easily verified in practice. Additionally, we contribute insights into the kind
of distribution shifts for which our approach promises robustness. Our
empirical evaluation demonstrates the effectiveness of our approach for both
low-dimensional and high-dimensional data sets. Finally, we demonstrate that we
can reliably detect scenarios where a model is tasked with unwarranted
extrapolation in out-of-distribution (OOD) domains, identifying potential
failure cases. Consequently, we showcase a method to select between the most
predictive and the most robust model, circumventing the well-known trade-off
between predictive performance and robustness.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Predictive Minds: LLMs As Atypical Active Inference Agents
Abstract: Large language models (LLMs) like GPT are often conceptualized as passive
predictors, simulators, or even stochastic parrots. We instead conceptualize
LLMs by drawing on the theory of active inference originating in cognitive
science and neuroscience. We examine similarities and differences between
traditional active inference systems and LLMs, leading to the conclusion that,
currently, LLMs lack a tight feedback loop between acting in the world and
perceiving the impacts of their actions, but otherwise fit in the active
inference paradigm. We list reasons why this loop may soon be closed, and
possible consequences of this including enhanced model self-awareness and the
drive to minimize prediction error by changing the world.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Detailed Human-Centric Text Description-Driven Large Scene Synthesis
Abstract: Text-driven large scene image synthesis has made significant progress with
diffusion models, but controlling it is challenging. While using additional
spatial controls with corresponding texts has improved the controllability of
large scene synthesis, it is still challenging to faithfully reflect detailed
text descriptions without user-provided controls. Here, we propose
DetText2Scene, a novel text-driven large-scale image synthesis with high
faithfulness, controllability, and naturalness in a global context for the
detailed human-centric text description. Our DetText2Scene consists of 1)
hierarchical keypoint-box layout generation from the detailed description by
leveraging large language model (LLM), 2) view-wise conditioned joint diffusion
process to synthesize a large scene from the given detailed text with
LLM-generated grounded keypoint-box layout and 3) pixel perturbation-based
pyramidal interpolation to progressively refine the large scene for global
coherence. Our DetText2Scene significantly outperforms prior arts in
text-to-large scene synthesis qualitatively and quantitatively, demonstrating
strong faithfulness with detailed descriptions, superior controllability, and
excellent naturalness in a global context.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Refine, Discriminate and Align: Stealing Encoders via Sample-Wise Prototypes and Multi-Relational Extraction
Abstract: This paper introduces RDA, a pioneering approach designed to address two
primary deficiencies prevalent in previous endeavors aiming at stealing
pre-trained encoders: (1) suboptimal performances attributed to biased
optimization objectives, and (2) elevated query costs stemming from the
end-to-end paradigm that necessitates querying the target encoder every epoch.
Specifically, we initially Refine the representations of the target encoder for
each training sample, thereby establishing a less biased optimization objective
before the steal-training phase. This is accomplished via a sample-wise
prototype, which consolidates the target encoder's representations for a given
sample's various perspectives. Demanding exponentially fewer queries compared
to the end-to-end approach, prototypes can be instantiated to guide subsequent
query-free training. For more potent efficacy, we develop a multi-relational
extraction loss that trains the surrogate encoder to Discriminate mismatched
embedding-prototype pairs while Aligning those matched ones in terms of both
amplitude and angle. In this way, the trained surrogate encoder achieves
state-of-the-art results across the board in various downstream datasets with
limited queries. Moreover, RDA is shown to be robust to multiple widely-used
defenses.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examples
Abstract: While vision-language models (VLMs) have achieved remarkable performance
improvements recently, there is growing evidence that these models also posses
harmful biases with respect to social attributes such as gender and race. Prior
studies have primarily focused on probing such bias attributes individually
while ignoring biases associated with intersections between social attributes.
This could be due to the difficulty of collecting an exhaustive set of
image-text pairs for various combinations of social attributes. To address this
challenge, we employ text-to-image diffusion models to produce counterfactual
examples for probing intserctional social biases at scale. Our approach
utilizes Stable Diffusion with cross attention control to produce sets of
counterfactual image-text pairs that are highly similar in their depiction of a
subject (e.g., a given occupation) while differing only in their depiction of
intersectional social attributes (e.g., race & gender). Through our
over-generate-then-filter methodology, we produce SocialCounterfactuals, a
high-quality dataset containing over 171k image-text pairs for probing
intersectional biases related to gender, race, and physical characteristics. We
conduct extensive experiments to demonstrate the usefulness of our generated
dataset for probing and mitigating intersectional social biases in
state-of-the-art VLMs.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Large Language Model-Driven Classroom Flipping: Empowering Student-Centric Peer Questioning with Flipped Interaction
Abstract: Reciprocal questioning is essential for effective teaching and learning,
fostering active engagement and deeper understanding through collaborative
interactions, especially in large classrooms. Can large language model (LLM),
such as OpenAI's GPT (Generative Pre-trained Transformer) series, assist in
this? This paper investigates a pedagogical approach of classroom flipping
based on flipped interaction in LLMs. Flipped interaction involves using
language models to prioritize generating questions instead of answers to
prompts. We demonstrate how traditional classroom flipping techniques,
including Peer Instruction and Just-in-Time Teaching (JiTT), can be enhanced
through flipped interaction techniques, creating student-centric questions for
hybrid teaching. In particular, we propose a workflow to integrate prompt
engineering with clicker and JiTT quizzes by a poll-prompt-quiz routine and a
quiz-prompt-discuss routine to empower students to self-regulate their learning
capacity and enable teachers to swiftly personalize training pathways. We
develop an LLM-driven chatbot software that digitizes various elements of
classroom flipping and facilitates the assessment of students using these
routines to deliver peer-generated questions. We have applied our LLM-driven
chatbot software for teaching both undergraduate and graduate students from
2020 to 2022, effectively useful for bridging the gap between teachers and
students in remote teaching during the COVID-19 pandemic years. In particular,
LLM-driven classroom flipping can be particularly beneficial in large class
settings to optimize teaching pace and enable engaging classroom experiences.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: CPSOR-GCN: A Vehicle Trajectory Prediction Method Powered by Emotion and Cognitive Theory
Abstract: Active safety systems on vehicles often face problems with false alarms. Most
active safety systems predict the driver's trajectory with the assumption that
the driver is always in a normal emotion, and then infer risks. However, the
driver's trajectory uncertainty increases under abnormal emotions. This paper
proposes a new trajectory prediction model: CPSOR-GCN, which predicts vehicle
trajectories under abnormal emotions. At the physical level, the interaction
features between vehicles are extracted by the physical GCN module. At the
cognitive level, SOR cognitive theory is used as prior knowledge to build a
Dynamic Bayesian Network (DBN) structure. The conditional probability and state
transition probability of nodes from the calibrated SOR-DBN quantify the causal
relationship between cognitive factors, which is embedded into the cognitive
GCN module to extract the characteristics of the influence mechanism of
emotions on driving behavior. The CARLA-SUMO joint driving simulation platform
was built to develop dangerous pre-crash scenarios. Methods of recreating
traffic scenes were used to naturally induce abnormal emotions. The experiment
collected data from 26 participants to verify the proposed model. Compared with
the model that only considers physical motion features, the prediction accuracy
of the proposed model is increased by 68.70%. Furthermore,considering the
SOR-DBN reduces the prediction error of the trajectory by 15.93%. Compared with
other advanced trajectory prediction models, the results of CPSOR-GCN also have
lower errors. This model can be integrated into active safety systems to better
adapt to the driver's emotions, which could effectively reduce false alarms.

Output: Artificial Intelligence