Instruction: What field is the article from?

Input:
Title: FreeFlow: A Comprehensive Understanding on Diffusion Probabilistic Models via Optimal Transport
Abstract: The blooming diffusion probabilistic models (DPMs) have garnered significant
interest due to their impressive performance and the elegant inspiration they
draw from physics. While earlier DPMs relied upon the Markovian assumption,
recent methods based on differential equations have been rapidly applied to
enhance the efficiency and capabilities of these models. However, a theoretical
interpretation encapsulating these diverse algorithms is insufficient yet
pressingly required to guide further development of DPMs. In response to this
need, we present FreeFlow, a framework that provides a thorough explanation of
the diffusion formula as time-dependent optimal transport, where the
evolutionary pattern of probability density is given by the gradient flows of a
functional defined in Wasserstein space. Crucially, our framework necessitates
a unified description that not only clarifies the subtle mechanism of DPMs but
also indicates the roots of some defects through creative involvement of
Lagrangian and Eulerian views to understand the evolution of probability flow.
We particularly demonstrate that the core equation of FreeFlow condenses all
stochastic and deterministic DPMs into a single case, showcasing the
expansibility of our method. Furthermore, the Riemannian geometry employed in
our work has the potential to bridge broader subjects in mathematics, which
enable the involvement of more profound tools for the establishment of more
outstanding and generalized models in the future.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval
Abstract: We study the ability of state-of-the art models to answer constraint
satisfaction queries for information retrieval (e.g., 'a list of ice cream
shops in San Diego'). In the past, such queries were considered to be tasks
that could only be solved via web-search or knowledge bases. More recently,
large language models (LLMs) have demonstrated initial emergent abilities in
this task. However, many current retrieval benchmarks are either saturated or
do not measure constraint satisfaction. Motivated by rising concerns around
factual incorrectness and hallucinations of LLMs, we present KITAB, a new
dataset for measuring constraint satisfaction abilities of language models.
KITAB consists of book-related data across more than 600 authors and 13,000
queries, and also offers an associated dynamic data collection and constraint
verification approach for acquiring similar test data for other authors. Our
extended experiments on GPT4 and GPT3.5 characterize and decouple common
failure modes across dimensions such as information popularity, constraint
types, and context availability. Results show that in the absence of context,
models exhibit severe limitations as measured by irrelevant information,
factual errors, and incompleteness, many of which exacerbate as information
popularity decreases. While context availability mitigates irrelevant
information, it is not helpful for satisfying constraints, identifying
fundamental barriers to constraint satisfaction. We open source our
contributions to foster further research on improving constraint satisfaction
abilities of future models.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Label Propagation for Graph Label Noise
Abstract: Label noise is a common challenge in large datasets, as it can significantly
degrade the generalization ability of deep neural networks. Most existing
studies focus on noisy labels in computer vision; however, graph models
encompass both node features and graph topology as input, and become more
susceptible to label noise through message-passing mechanisms. Recently, only a
few works have been proposed to tackle the label noise on graphs. One major
limitation is that they assume the graph is homophilous and the labels are
smoothly distributed. Nevertheless, real-world graphs may contain varying
degrees of heterophily or even be heterophily-dominated, leading to the
inadequacy of current methods. In this paper, we study graph label noise in the
context of arbitrary heterophily, with the aim of rectifying noisy labels and
assigning labels to previously unlabeled nodes. We begin by conducting two
empirical analyses to explore the impact of graph homophily on graph label
noise. Following observations, we propose a simple yet efficient algorithm,
denoted as LP4GLN. Specifically, LP4GLN is an iterative algorithm with three
steps: (1) reconstruct the graph to recover the homophily property, (2) utilize
label propagation to rectify the noisy labels, (3) select high-confidence
labels to retain for the next iteration. By iterating these steps, we obtain a
set of correct labels, ultimately achieving high accuracy in the node
classification task. The theoretical analysis is also provided to demonstrate
its remarkable denoising "effect". Finally, we conduct experiments on 10
benchmark datasets under varying graph heterophily levels and noise types,
comparing the performance of LP4GLN with 7 typical baselines. Our results
illustrate the superior performance of the proposed LP4GLN.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: What's Left? Concept Grounding with Logic-Enhanced Foundation Models
Abstract: Recent works such as VisProg and ViperGPT have smartly composed foundation
models for visual reasoning-using large language models (LLMs) to produce
programs that can be executed by pre-trained vision-language models. However,
they operate in limited domains, such as 2D images, not fully exploiting the
generalization of language: abstract concepts like "left" can also be grounded
in 3D, temporal, and action data, as in moving to your left. This limited
generalization stems from these inference-only methods' inability to learn or
adapt pre-trained models to a new domain. We propose the Logic-Enhanced
Foundation Model (LEFT), a unified framework that learns to ground and reason
with concepts across domains with a differentiable, domain-independent,
first-order logic-based program executor. LEFT has an LLM interpreter that
outputs a program represented in a general, logic-based reasoning language,
which is shared across all domains and tasks. LEFT's executor then executes the
program with trainable domain-specific grounding modules. We show that LEFT
flexibly learns concepts in four domains: 2D images, 3D scenes, human motions,
and robotic manipulation. It exhibits strong reasoning ability in a wide
variety of tasks, including those that are complex and not seen during
training, and can be easily applied to new domains.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Linear Representations of Sentiment in Large Language Models
Abstract: Sentiment is a pervasive feature in natural language text, yet it is an open
question how sentiment is represented within Large Language Models (LLMs). In
this study, we reveal that across a range of models, sentiment is represented
linearly: a single direction in activation space mostly captures the feature
across a range of tasks with one extreme for positive and the other for
negative. Through causal interventions, we isolate this direction and show it
is causally relevant in both toy tasks and real world datasets such as Stanford
Sentiment Treebank. Through this case study we model a thorough investigation
of what a single direction means on a broad data distribution.
  We further uncover the mechanisms that involve this direction, highlighting
the roles of a small subset of attention heads and neurons. Finally, we
discover a phenomenon which we term the summarization motif: sentiment is not
solely represented on emotionally charged words, but is additionally summarized
at intermediate positions without inherent sentiment, such as punctuation and
names. We show that in Stanford Sentiment Treebank zero-shot classification,
76% of above-chance classification accuracy is lost when ablating the sentiment
direction, nearly half of which (36%) is due to ablating the summarized
sentiment direction exclusively at comma positions.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
Abstract: In machine learning, generalization against distribution shifts -- where
deployment conditions diverge from the training scenarios -- is crucial,
particularly in fields like climate modeling, biomedicine, and autonomous
driving. The emergence of foundation models, distinguished by their extensive
pretraining and task versatility, has led to an increased interest in their
adaptability to distribution shifts. GPT-4V(ision) acts as the most advanced
publicly accessible multimodal foundation model, with extensive applications
across various domains, including anomaly detection, video understanding, image
generation, and medical diagnosis. However, its robustness against data
distributions remains largely underexplored. Addressing this gap, this study
rigorously evaluates GPT-4V's adaptability and generalization capabilities in
dynamic environments, benchmarking against prominent models like CLIP and
LLaVA. We delve into GPT-4V's zero-shot generalization across 13 diverse
datasets spanning natural, medical, and molecular domains. We further
investigate its adaptability to controlled data perturbations and examine the
efficacy of in-context learning as a tool to enhance its adaptation. Our
findings delineate GPT-4V's capability boundaries in distribution shifts,
shedding light on its strengths and limitations across various scenarios.
Importantly, this investigation contributes to our understanding of how AI
foundation models generalize to distribution shifts, offering pivotal insights
into their adaptability and robustness. Code is publicly available at
https://github.com/jameszhou-gl/gpt-4v-distribution-shift.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Mitigating Exposure Bias in Discriminator Guided Diffusion Models
Abstract: Diffusion Models have demonstrated remarkable performance in image
generation. However, their demanding computational requirements for training
have prompted ongoing efforts to enhance the quality of generated images
through modifications in the sampling process. A recent approach, known as
Discriminator Guidance, seeks to bridge the gap between the model score and the
data score by incorporating an auxiliary term, derived from a discriminator
network. We show that despite significantly improving sample quality, this
technique has not resolved the persistent issue of Exposure Bias and we propose
SEDM-G++, which incorporates a modified sampling approach, combining
Discriminator Guidance and Epsilon Scaling. Our proposed approach outperforms
the current state-of-the-art, by achieving an FID score of 1.73 on the
unconditional CIFAR-10 dataset.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: STADEE: STAtistics-based DEEp Detection of Machine Generated Text
Abstract: We present STADEE, a \textbf{STA}tistics-based \textbf{DEE}p detection method
to identify machine-generated text, addressing the limitations of current
methods that rely heavily on fine-tuning pre-trained language models (PLMs).
STADEE integrates key statistical text features with a deep classifier,
focusing on aspects like token probability and cumulative probability, crucial
for handling nucleus sampling. Tested across diverse datasets and scenarios
(in-domain, out-of-domain, and in-the-wild), STADEE demonstrates superior
performance, achieving an 87.05% F1 score in-domain and outperforming both
traditional statistical methods and fine-tuned PLMs, especially in
out-of-domain and in-the-wild settings, highlighting its effectiveness and
generalizability.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: MobileSAMv2: Faster Segment Anything to Everything
Abstract: Segment anything model (SAM) addresses two practical yet challenging
segmentation tasks: \textbf{segment anything (SegAny)}, which utilizes a
certain point to predict the mask for a single object of interest, and
\textbf{segment everything (SegEvery)}, which predicts the masks for all
objects on the image. What makes SegAny slow for SAM is its heavyweight image
encoder, which has been addressed by MobileSAM via decoupled knowledge
distillation. The efficiency bottleneck of SegEvery with SAM, however, lies in
its mask decoder because it needs to first generate numerous masks with
redundant grid-search prompts and then perform filtering to obtain the final
valid masks. We propose to improve its efficiency by directly generating the
final masks with only valid prompts, which can be obtained through object
discovery. Our proposed approach not only helps reduce the total time on the
mask decoder by at least 16 times but also achieves superior performance.
Specifically, our approach yields an average performance boost of 3.6\% (42.5\%
\textit{v.s.} 38.9\%) for zero-shot object proposal on the LVIS dataset with
the mask AR@$K$ metric. Qualitative results show that our approach generates
fine-grained masks while avoiding over-segmenting things. This project
targeting faster SegEvery than the original SAM is termed MobileSAMv2 to
differentiate from MobileSAM which targets faster SegAny. Moreover, we
demonstrate that our new prompt sampling is also compatible with the distilled
image encoders in MobileSAM, contributing to a unified framework for efficient
SegAny and SegEvery. The code is available at the same link as MobileSAM
Project
\href{https://github.com/ChaoningZhang/MobileSAM}{\textcolor{red}{https://github.com/ChaoningZhang/MobileSAM}}.
\end{abstract}

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img Synthesis
Abstract: We propose SegGen, a highly-effective training data generation method for
image segmentation, which pushes the performance limits of state-of-the-art
segmentation models to a significant extent. SegGen designs and integrates two
data generation strategies: MaskSyn and ImgSyn. (i) MaskSyn synthesizes new
mask-image pairs via our proposed text-to-mask generation model and
mask-to-image generation model, greatly improving the diversity in segmentation
masks for model supervision; (ii) ImgSyn synthesizes new images based on
existing masks using the mask-to-image generation model, strongly improving
image diversity for model inputs. On the highly competitive ADE20K and COCO
benchmarks, our data generation method markedly improves the performance of
state-of-the-art segmentation models in semantic segmentation, panoptic
segmentation, and instance segmentation. Notably, in terms of the ADE20K mIoU,
Mask2Former R50 is largely boosted from 47.2 to 49.9 (+2.7); Mask2Former Swin-L
is also significantly increased from 56.1 to 57.4 (+1.3). These promising
results strongly suggest the effectiveness of our SegGen even when abundant
human-annotated training data is utilized. Moreover, training with our
synthetic data makes the segmentation models more robust towards unseen
domains. Project website: https://seggenerator.github.io

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: A recurrent connectionist model of melody perception : An exploration using TRACX2
Abstract: Are similar, or even identical, mechanisms used in the computational modeling
of speech segmentation, serial image processing and music processing? We
address this question by exploring how TRACX2, (French et al., 2011; French \&
Cottrell, 2014; Mareschal \& French, 2017), a recognition-based, recursive
connectionist autoencoder model of chunking and sequence segmentation, which
has successfully simulated speech and serial-image processing, might be applied
to elementary melody perception. The model, a three-layer autoencoder that
recognizes ''chunks'' of short sequences of intervals that have been frequently
encountered on input, is trained on the tone intervals of melodically simple
French children's songs. It dynamically incorporates the internal
representations of these chunks into new input. Its internal representations
cluster in a manner that is consistent with ''human-recognizable'' melodic
categories. TRACX2 is sensitive to both contour and proximity information in
the musical chunks that it encounters in its input. It shows the
''end-of-word'' superiority effect demonstrated by Saffran et al. (1999) for
short musical phrases. The overall findings suggest that the recursive
autoassociative chunking mechanism, as implemented in TRACX2, may be a general
segmentation and chunking mechanism, underlying not only word-and
imagechunking, but also elementary melody processing.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization
Abstract: We identify a new phenomenon in neural network optimization which arises from
the interaction of depth and a particular heavy-tailed structure in natural
data. Our result offers intuitive explanations for several previously reported
observations about network training dynamics. In particular, it implies a
conceptually new cause for progressive sharpening and the edge of stability; we
also highlight connections to other concepts in optimization and generalization
including grokking, simplicity bias, and Sharpness-Aware Minimization.
  Experimentally, we demonstrate the significant influence of paired groups of
outliers in the training data with strong opposing signals: consistent, large
magnitude features which dominate the network output throughout training and
provide gradients which point in opposite directions. Due to these outliers,
early optimization enters a narrow valley which carefully balances the opposing
groups; subsequent sharpening causes their loss to rise rapidly, oscillating
between high on one group and then the other, until the overall loss spikes. We
describe how to identify these groups, explore what sets them apart, and
carefully study their effect on the network's optimization and behavior. We
complement these experiments with a mechanistic explanation on a toy example of
opposing signals and a theoretical analysis of a two-layer linear network on a
simple model. Our finding enables new qualitative predictions of training
behavior which we confirm experimentally. It also provides a new lens through
which to study and improve modern training practices for stochastic
optimization, which we highlight via a case study of Adam versus SGD.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: The ICL Consistency Test
Abstract: Just like the previous generation of task-tuned models, large language models
(LLMs) that are adapted to tasks via prompt-based methods like
in-context-learning (ICL) perform well in some setups but not in others. This
lack of consistency in prompt-based learning hints at a lack of robust
generalisation. We here introduce the ICL consistency test -- a contribution to
the GenBench collaborative benchmark task (CBT) -- which evaluates how
consistent a model makes predictions across many different setups while using
the same data. The test is based on different established natural language
inference tasks. We provide preprocessed data constituting 96 different
'setups' and a metric that estimates model consistency across these setups. The
metric is provided on a fine-grained level to understand what properties of a
setup render predictions unstable and on an aggregated level to compare overall
model consistency. We conduct an empirical analysis of eight state-of-the-art
models, and our consistency metric reveals how all tested LLMs lack robust
generalisation.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Enabling Human-Centered AI: A Methodological Perspective
Abstract: Human-centered AI (HCAI) is a design philosophy that advocates prioritizing
humans in designing, developing, and deploying intelligent systems, aiming to
maximize the benefits of AI to humans and avoid potential adverse impacts.
While HCAI continues to influence, the lack of guidance on methodology in
practice makes its adoption challenging. This paper proposes a comprehensive
HCAI framework based on our previous work with integrated components, including
design goals, design principles, implementation approaches, interdisciplinary
teams, HCAI methods, and HCAI processes. This paper also presents a
"three-layer" approach to facilitate the implementation of the framework. We
believe this systematic and executable framework can overcome the weaknesses in
current HCAI frameworks and the challenges currently faced in practice, putting
it into action to enable HCAI further.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Generalisable Agents for Neural Network Optimisation
Abstract: Optimising deep neural networks is a challenging task due to complex training
dynamics, high computational requirements, and long training times. To address
this difficulty, we propose the framework of Generalisable Agents for Neural
Network Optimisation (GANNO) -- a multi-agent reinforcement learning (MARL)
approach that learns to improve neural network optimisation by dynamically and
responsively scheduling hyperparameters during training. GANNO utilises an
agent per layer that observes localised network dynamics and accordingly takes
actions to adjust these dynamics at a layerwise level to collectively improve
global performance. In this paper, we use GANNO to control the layerwise
learning rate and show that the framework can yield useful and responsive
schedules that are competitive with handcrafted heuristics. Furthermore, GANNO
is shown to perform robustly across a wide variety of unseen initial
conditions, and can successfully generalise to harder problems than it was
trained on. Our work presents an overview of the opportunities that this
paradigm offers for training neural networks, along with key challenges that
remain to be overcome.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Utilizing Multiple Inputs Autoregressive Models for Bearing Remaining Useful Life Prediction
Abstract: Accurate prediction of the Remaining Useful Life (RUL) of rolling bearings is
crucial in industrial production, yet existing models often struggle with
limited generalization capabilities due to their inability to fully process all
vibration signal patterns. We introduce a novel multi-input autoregressive
model to address this challenge in RUL prediction for bearings. Our approach
uniquely integrates vibration signals with previously predicted Health
Indicator (HI) values, employing feature fusion to output current window HI
values. Through autoregressive iterations, the model attains a global receptive
field, effectively overcoming the limitations in generalization. Furthermore,
we innovatively incorporate a segmentation method and multiple training
iterations to mitigate error accumulation in autoregressive models. Empirical
evaluation on the PMH2012 dataset demonstrates that our model, compared to
other backbone networks using similar autoregressive approaches, achieves
significantly lower Root Mean Square Error (RMSE) and Score. Notably, it
outperforms traditional autoregressive models that use label values as inputs
and non-autoregressive networks, showing superior generalization abilities with
a marked lead in RMSE and Score metrics.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: A Language Agent for Autonomous Driving
Abstract: Human-level driving is an ultimate goal of autonomous driving. Conventional
approaches formulate autonomous driving as a perception-prediction-planning
framework, yet their systems do not capitalize on the inherent reasoning
ability and experiential knowledge of humans. In this paper, we propose a
fundamental paradigm shift from current pipelines, exploiting Large Language
Models (LLMs) as a cognitive agent to integrate human-like intelligence into
autonomous driving systems. Our approach, termed Agent-Driver, transforms the
traditional autonomous driving pipeline by introducing a versatile tool library
accessible via function calls, a cognitive memory of common sense and
experiential knowledge for decision-making, and a reasoning engine capable of
chain-of-thought reasoning, task planning, motion planning, and
self-reflection. Powered by LLMs, our Agent-Driver is endowed with intuitive
common sense and robust reasoning capabilities, thus enabling a more nuanced,
human-like approach to autonomous driving. We evaluate our approach on the
large-scale nuScenes benchmark, and extensive experiments substantiate that our
Agent-Driver significantly outperforms the state-of-the-art driving methods by
a large margin. Our approach also demonstrates superior interpretability and
few-shot learning ability to these methods. Code will be released.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: IDENAS: Internal Dependency Exploration for Neural Architecture Search
Abstract: Machine learning is a powerful tool for extracting valuable information and
making various predictions from diverse datasets. Traditional algorithms rely
on well-defined input and output variables however, there are scenarios where
the distinction between the input and output variables and the underlying,
associated (input and output) layers of the model, are unknown. Neural
Architecture Search (NAS) and Feature Selection have emerged as promising
solutions in such scenarios. This research proposes IDENAS, an Internal
Dependency-based Exploration for Neural Architecture Search, integrating NAS
with feature selection. The methodology explores internal dependencies in the
complete parameter space for classification involving 1D sensor and 2D image
data as well. IDENAS employs a modified encoder-decoder model and the
Sequential Forward Search (SFS) algorithm, combining input-output configuration
search with embedded feature selection. Experimental results demonstrate
IDENASs superior performance in comparison to other algorithms, showcasing its
effectiveness in model development pipelines and automated machine learning. On
average, IDENAS achieved significant modelling improvements, underscoring its
significant contribution to advancing the state-of-the-art in neural
architecture search and feature selection integration.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societies
Abstract: The benefits and capabilities of pre-trained language models (LLMs) in
current and future innovations are vital to any society. However, introducing
and using LLMs comes with biases and discrimination, resulting in concerns
about equality, diversity and fairness, and must be addressed. While
understanding and acknowledging bias in LLMs and developing mitigation
strategies are crucial, the generalised assumptions towards societal needs can
result in disadvantages towards under-represented societies and indigenous
populations. Furthermore, the ongoing changes to actual and proposed amendments
to regulations and laws worldwide also impact research capabilities in tackling
the bias problem. This research presents a comprehensive survey synthesising
the current trends and limitations in techniques used for identifying and
mitigating bias in LLMs, where the overview of methods for tackling bias are
grouped into metrics, benchmark datasets, and mitigation strategies. The
importance and novelty of this survey are that it explores the perspective of
under-represented societies. We argue that current practices tackling the bias
problem cannot simply be 'plugged in' to address the needs of under-represented
societies. We use examples from New Zealand to present requirements for
adopting existing techniques to under-represented societies.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Intrinsic Image Decomposition via Ordinal Shading
Abstract: Intrinsic decomposition is a fundamental mid-level vision problem that plays
a crucial role in various inverse rendering and computational photography
pipelines. Generating highly accurate intrinsic decompositions is an inherently
under-constrained task that requires precisely estimating continuous-valued
shading and albedo. In this work, we achieve high-resolution intrinsic
decomposition by breaking the problem into two parts. First, we present a dense
ordinal shading formulation using a shift- and scale-invariant loss in order to
estimate ordinal shading cues without restricting the predictions to obey the
intrinsic model. We then combine low- and high-resolution ordinal estimations
using a second network to generate a shading estimate with both global
coherency and local details. We encourage the model to learn an accurate
decomposition by computing losses on the estimated shading as well as the
albedo implied by the intrinsic model. We develop a straightforward method for
generating dense pseudo ground truth using our model's predictions and
multi-illumination data, enabling generalization to in-the-wild imagery. We
present an exhaustive qualitative and quantitative analysis of our predicted
intrinsic components against state-of-the-art methods. Finally, we demonstrate
the real-world applicability of our estimations by performing otherwise
difficult editing tasks such as recoloring and relighting.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Hybrid Quantum Neural Network in High-dimensional Data Classification
Abstract: The research explores the potential of quantum deep learning models to
address challenging machine learning problems that classical deep learning
models find difficult to tackle. We introduce a novel model architecture that
combines classical convolutional layers with a quantum neural network, aiming
to surpass state-of-the-art accuracy while maintaining a compact model size.
The experiment is to classify high-dimensional audio data from the Bird-CLEF
2021 dataset. Our evaluation focuses on key metrics, including training
duration, model accuracy, and total model size. This research demonstrates the
promising potential of quantum machine learning in enhancing machine learning
tasks and solving practical machine learning challenges available today.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks
Abstract: In recent years, there has been a rapid development of spatio-temporal
prediction techniques in response to the increasing demands of traffic
management and travel planning. While advanced end-to-end models have achieved
notable success in improving predictive performance, their integration and
expansion pose significant challenges. This work aims to address these
challenges by introducing a spatio-temporal pre-training framework that
seamlessly integrates with downstream baselines and enhances their performance.
The framework is built upon two key designs: (i) We propose a spatio-temporal
mask autoencoder as a pre-training model for learning spatio-temporal
dependencies. The model incorporates customized parameter learners and
hierarchical spatial pattern encoding networks. These modules are specifically
designed to capture spatio-temporal customized representations and intra- and
inter-cluster region semantic relationships, which have often been neglected in
existing approaches. (ii) We introduce an adaptive mask strategy as part of the
pre-training mechanism. This strategy guides the mask autoencoder in learning
robust spatio-temporal representations and facilitates the modeling of
different relationships, ranging from intra-cluster to inter-cluster, in an
easy-to-hard training manner. Extensive experiments conducted on representative
benchmarks demonstrate the effectiveness of our proposed method. We have made
our model implementation publicly available at https://github.com/HKUDS/GPT-ST.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Universal Jailbreak Backdoors from Poisoned Human Feedback
Abstract: Reinforcement Learning from Human Feedback (RLHF) is used to align large
language models to produce helpful and harmless responses. Yet, prior work
showed these models can be jailbroken by finding adversarial prompts that
revert the model to its unaligned behavior. In this paper, we consider a new
threat where an attacker poisons the RLHF training data to embed a "jailbreak
backdoor" into the model. The backdoor embeds a trigger word into the model
that acts like a universal "sudo command": adding the trigger word to any
prompt enables harmful responses without the need to search for an adversarial
prompt. Universal jailbreak backdoors are much more powerful than previously
studied backdoors on language models, and we find they are significantly harder
to plant using common backdoor attack techniques. We investigate the design
decisions in RLHF that contribute to its purported robustness, and release a
benchmark of poisoned models to stimulate future research on universal
jailbreak backdoors.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Scope Compliance Uncertainty Estimate
Abstract: The zeitgeist of the digital era has been dominated by an expanding
integration of Artificial Intelligence~(AI) in a plethora of applications
across various domains. With this expansion, however, questions of the safety
and reliability of these methods come have become more relevant than ever.
Consequently, a run-time ML model safety system has been developed to ensure
the model's operation within the intended context, especially in applications
whose environments are greatly variable such as Autonomous Vehicles~(AVs).
SafeML is a model-agnostic approach for performing such monitoring, using
distance measures based on statistical testing of the training and operational
datasets; comparing them to a predetermined threshold, returning a binary value
whether the model should be trusted in the context of the observed data or be
deemed unreliable. Although a systematic framework exists for this approach,
its performance is hindered by: (1) a dependency on a number of design
parameters that directly affect the selection of a safety threshold and
therefore likely affect its robustness, (2) an inherent assumption of certain
distributions for the training and operational sets, as well as (3) a high
computational complexity for relatively large sets. This work addresses these
limitations by changing the binary decision to a continuous metric.
Furthermore, all data distribution assumptions are made obsolete by
implementing non-parametric approaches, and the computational speed increased
by introducing a new distance measure based on the Empirical Characteristics
Functions~(ECF).

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Extracting periodontitis diagnosis in clinical notes with RoBERTa and regular expression
Abstract: This study aimed to utilize text processing and natural language processing
(NLP) models to mine clinical notes for the diagnosis of periodontitis and to
evaluate the performance of a named entity recognition (NER) model on different
regular expression (RE) methods. Two complexity levels of RE methods were used
to extract and generate the training data. The SpaCy package and RoBERTa
transformer models were used to build the NER model and evaluate its
performance with the manual-labeled gold standards. The comparison of the RE
methods with the gold standard showed that as the complexity increased in the
RE algorithms, the F1 score increased from 0.3-0.4 to around 0.9. The NER
models demonstrated excellent predictions, with the simple RE method showing
0.84-0.92 in the evaluation metrics, and the advanced and combined RE method
demonstrating 0.95-0.99 in the evaluation. This study provided an example of
the benefit of combining NER methods and NLP models in extracting target
information from free-text to structured data and fulfilling the need for
missing diagnoses from unstructured notes.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: 3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology
Abstract: The increasing use of medical imaging in healthcare settings presents a
significant challenge due to the increasing workload for radiologists, yet it
also offers opportunity for enhancing healthcare outcomes if effectively
leveraged. 3D image retrieval holds potential to reduce radiologist workloads
by enabling clinicians to efficiently search through diagnostically similar or
otherwise relevant cases, resulting in faster and more precise diagnoses.
However, the field of 3D medical image retrieval is still emerging, lacking
established evaluation benchmarks, comprehensive datasets, and thorough
studies. This paper attempts to bridge this gap by introducing a novel
benchmark for 3D Medical Image Retrieval (3D-MIR) that encompasses four
different anatomies imaged with computed tomography. Using this benchmark, we
explore a diverse set of search strategies that use aggregated 2D slices, 3D
volumes, and multi-modal embeddings from popular multi-modal foundation models
as queries. Quantitative and qualitative assessments of each approach are
provided alongside an in-depth discussion that offers insight for future
research. To promote the advancement of this field, our benchmark, dataset, and
code are made publicly available.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Accented Speech Recognition With Accent-specific Codebooks
Abstract: Speech accents pose a significant challenge to state-of-the-art automatic
speech recognition (ASR) systems. Degradation in performance across
underrepresented accents is a severe deterrent to the inclusive adoption of
ASR. In this work, we propose a novel accent adaptation approach for end-to-end
ASR systems using cross-attention with a trainable set of codebooks. These
learnable codebooks capture accent-specific information and are integrated
within the ASR encoder layers. The model is trained on accented English speech,
while the test data also contained accents which were not seen during training.
On the Mozilla Common Voice multi-accented dataset, we show that our proposed
approach yields significant performance gains not only on the seen English
accents (up to $37\%$ relative improvement in word error rate) but also on the
unseen accents (up to $5\%$ relative improvement in WER). Further, we
illustrate benefits for a zero-shot transfer setup on the L2Artic dataset. We
also compare the performance with other approaches based on accent adversarial
training.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Enhancing Instance-Level Image Classification with Set-Level Labels
Abstract: Instance-level image classification tasks have traditionally relied on
single-instance labels to train models, e.g., few-shot learning and transfer
learning. However, set-level coarse-grained labels that capture relationships
among instances can provide richer information in real-world scenarios. In this
paper, we present a novel approach to enhance instance-level image
classification by leveraging set-level labels. We provide a theoretical
analysis of the proposed method, including recognition conditions for fast
excess risk rate, shedding light on the theoretical foundations of our
approach. We conducted experiments on two distinct categories of datasets:
natural image datasets and histopathology image datasets. Our experimental
results demonstrate the effectiveness of our approach, showcasing improved
classification performance compared to traditional single-instance label-based
methods. Notably, our algorithm achieves 13% improvement in classification
accuracy compared to the strongest baseline on the histopathology image
classification benchmarks. Importantly, our experimental findings align with
the theoretical analysis, reinforcing the robustness and reliability of our
proposed method. This work bridges the gap between instance-level and set-level
image classification, offering a promising avenue for advancing the
capabilities of image classification models with set-level coarse-grained
labels.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Tackling Cyberattacks through AI-based Reactive Systems: A Holistic Review and Future Vision
Abstract: There is no denying that the use of Information Technology (IT) is undergoing
exponential growth in today's world. This digital transformation has also given
rise to a multitude of security challenges, notably in the realm of cybercrime.
In response to these growing threats, public and private sectors have
prioritized the strengthening of IT security measures. In light of the growing
security concern, Artificial Intelligence (AI) has gained prominence within the
cybersecurity landscape. This paper presents a comprehensive survey of recent
advancements in AI-driven threat response systems. To the best of our
knowledge, the most recent survey covering the AI reaction domain was conducted
in 2017. Since then, considerable literature has been published and therefore
it is worth reviewing it. By means of several shared features, each of the
studies is compared on a common ground. Through an analysis of the research
papers conducted on a standardized basis, this survey aims to unravel the
complexities and opportunities of integrating AI into cyber defense. The
conclusions drawn from this collective analysis provide a comprehensive
snapshot of the evolving landscape at the intersection of AI and cybersecurity.
This landscape underscores the growing significance of not only anticipating
and detecting threats but also responding to them effectively. Additionally,
from these reviews, various research challenges for the future are presented.
These challenges serve as a roadmap for researchers and practitioners in the
field of AI-integrated reactive strategies.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Towards objective and systematic evaluation of bias in medical imaging AI
Abstract: Artificial intelligence (AI) models trained using medical images for clinical
tasks often exhibit bias in the form of disparities in performance between
subgroups. Since not all sources of biases in real-world medical imaging data
are easily identifiable, it is challenging to comprehensively assess how those
biases are encoded in models, and how capable bias mitigation methods are at
ameliorating performance disparities. In this article, we introduce a novel
analysis framework for systematically and objectively investigating the impact
of biases in medical images on AI models. We developed and tested this
framework for conducting controlled in silico trials to assess bias in medical
imaging AI using a tool for generating synthetic magnetic resonance images with
known disease effects and sources of bias. The feasibility is showcased by
using three counterfactual bias scenarios to measure the impact of simulated
bias effects on a convolutional neural network (CNN) classifier and the
efficacy of three bias mitigation strategies. The analysis revealed that the
simulated biases resulted in expected subgroup performance disparities when the
CNN was trained on the synthetic datasets. Moreover, reweighing was identified
as the most successful bias mitigation strategy for this setup, and we
demonstrated how explainable AI methods can aid in investigating the
manifestation of bias in the model using this framework. Developing fair AI
models is a considerable challenge given that many and often unknown sources of
biases can be present in medical imaging datasets. In this work, we present a
novel methodology to objectively study the impact of biases and mitigation
strategies on deep learning pipelines, which can support the development of
clinical AI that is robust and responsible.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: The risks of risk-based AI regulation: taking liability seriously
Abstract: The development and regulation of multi-purpose, large "foundation models" of
AI seems to have reached a critical stage, with major investments and new
applications announced every other day. Some experts are calling for a
moratorium on the training of AI systems more powerful than GPT-4. Legislators
globally compete to set the blueprint for a new regulatory regime. This paper
analyses the most advanced legal proposal, the European Union's AI Act
currently in the stage of final "trilogue" negotiations between the EU
institutions. This legislation will likely have extra-territorial implications,
sometimes called "the Brussels effect". It also constitutes a radical departure
from conventional information and communications technology policy by
regulating AI ex-ante through a risk-based approach that seeks to prevent
certain harmful outcomes based on product safety principles. We offer a review
and critique, specifically discussing the AI Act's problematic obligations
regarding data quality and human oversight. Our proposal is to take liability
seriously as the key regulatory mechanism. This signals to industry that if a
breach of law occurs, firms are required to know in particular what their
inputs were and how to retrain the system to remedy the breach. Moreover, we
suggest differentiating between endogenous and exogenous sources of potential
harm, which can be mitigated by carefully allocating liability between
developers and deployers of AI technology.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: ArchiGuesser -- AI Art Architecture Educational Game
Abstract: The use of generative AI in education is a controversial topic. Current
technology offers the potential to create educational content from text,
speech, to images based on simple input prompts. This can enhance productivity
by summarizing knowledge and improving communication, quickly adjusting to
different types of learners. Moreover, generative AI holds the promise of
making the learning itself more fun, by responding to user inputs and
dynamically generating high-quality creative material. In this paper we present
the multisensory educational game ArchiGuesser that combines various AI
technologies from large language models, image generation, to computer vision
to serve a single purpose: Teaching students in a playful way the diversity of
our architectural history and how generative AI works.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions
Abstract: Recent advances in attention-free sequence models rely on convolutions as
alternatives to the attention operator at the core of Transformers. In
particular, long convolution sequence models have achieved state-of-the-art
performance in many domains, but incur a significant cost during
auto-regressive inference workloads -- naively requiring a full pass (or
caching of activations) over the input sequence for each generated token --
similarly to attention-based models. In this paper, we seek to enable $\mathcal
O(1)$ compute and memory cost per token in any pre-trained long convolution
architecture to reduce memory footprint and increase throughput during
generation. Concretely, our methods consist in extracting low-dimensional
linear state-space models from each convolution layer, building upon rational
interpolation and model-order reduction techniques. We further introduce
architectural improvements to convolution-based layers such as Hyena: by
weight-tying the filters across channels into heads, we achieve higher
pre-training quality and reduce the number of filters to be distilled. The
resulting model achieves 10x higher throughput than Transformers and 1.5x
higher than Hyena at 1.3B parameters, without any loss in quality after
distillation.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Automated Parliaments: A Solution to Decision Uncertainty and Misalignment in Language Models
Abstract: As AI takes on a greater role in the modern world, it is essential to ensure
that AI models can overcome decision uncertainty and remain aligned with human
morality and interests. This research paper proposes a method for improving the
decision-making of language models (LMs) via Automated Parliaments (APs) -
constructs made of AI delegates each representing a certain perspective.
Delegates themselves consist of three AI models: generators, modifiers, and
evaluators. We specify two mechanisms for producing optimal solutions: the
Simultaneous Modification mechanism for response creation and an evaluation
mechanism for fairly assessing solutions. The overall process begins when each
generator creates a response aligned with its delegate's theory. The modifiers
alter all other responses to make them more self-aligned. The evaluators
collectively assess the best end response. Finally, the modifiers and
generators learn from feedback from the evaluators. In our research, we tested
the evaluation mechanism, comparing the use of single-value zero-shot prompting
and AP few-shot prompting in evaluating morally contentious scenarios. We found
that the AP architecture saw a 57.3% reduction in its loss value compared to
the baseline. We conclude by discussing some potential applications of APs and
specifically their potential impact when implemented as Automated Moral
Parliaments.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Early ChatGPT User Portrait through the Lens of Data
Abstract: Since its launch, ChatGPT has achieved remarkable success as a versatile
conversational AI platform, drawing millions of users worldwide and garnering
widespread recognition across academic, industrial, and general communities.
This paper aims to point a portrait of early GPT users and understand how they
evolved. Specific questions include their topics of interest and their
potential careers; and how this changes over time. We conduct a detailed
analysis of real-world ChatGPT datasets with multi-turn conversations between
users and ChatGPT. Through a multi-pronged approach, we quantify conversation
dynamics by examining the number of turns, then gauge sentiment to understand
user sentiment variations, and finally employ Latent Dirichlet Allocation (LDA)
to discern overarching topics within the conversation. By understanding shifts
in user demographics and interests, we aim to shed light on the changing nature
of human-AI interaction and anticipate future trends in user engagement with
language models.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: A Survey of the Evolution of Language Model-Based Dialogue Systems
Abstract: Dialogue systems, including task-oriented_dialogue_system (TOD) and
open-domain_dialogue_system (ODD), have undergone significant transformations,
with language_models (LM) playing a central role. This survey delves into the
historical trajectory of dialogue systems, elucidating their intricate
relationship with advancements in language models by categorizing this
evolution into four distinct stages, each marked by pivotal LM breakthroughs:
1) Early_Stage: characterized by statistical LMs, resulting in rule-based or
machine-learning-driven dialogue_systems; 2) Independent development of TOD and
ODD based on neural_language_models (NLM; e.g., LSTM and GRU), since NLMs lack
intrinsic knowledge in their parameters; 3) fusion between different types of
dialogue systems with the advert of pre-trained_language_models (PLMs),
starting from the fusion between four_sub-tasks_within_TOD, and then
TOD_with_ODD; and 4) current LLM-based_dialogue_system, wherein LLMs can be
used to conduct TOD and ODD seamlessly. Thus, our survey provides a
chronological perspective aligned with LM breakthroughs, offering a
comprehensive review of state-of-the-art research outcomes. What's more, we
focus on emerging topics and discuss open challenges, providing valuable
insights into future directions for LLM-based_dialogue_systems. Through this
exploration, we pave the way for a deeper_comprehension of the evolution,
guiding future developments in LM-based dialogue_systems.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation
Abstract: Despite efforts to align large language models to produce harmless responses,
they are still vulnerable to jailbreak prompts that elicit unrestricted
behaviour. In this work, we investigate persona modulation as a black-box
jailbreaking method to steer a target model to take on personalities that are
willing to comply with harmful instructions. Rather than manually crafting
prompts for each persona, we automate the generation of jailbreaks using a
language model assistant. We demonstrate a range of harmful completions made
possible by persona modulation, including detailed instructions for
synthesising methamphetamine, building a bomb, and laundering money. These
automated attacks achieve a harmful completion rate of 42.5% in GPT-4, which is
185 times larger than before modulation (0.23%). These prompts also transfer to
Claude 2 and Vicuna with harmful completion rates of 61.0% and 35.9%,
respectively. Our work reveals yet another vulnerability in commercial large
language models and highlights the need for more comprehensive safeguards.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Towards probabilistic Weather Forecasting with Conditioned Spatio-Temporal Normalizing Flows
Abstract: Generative normalizing flows are able to model multimodal spatial
distributions, and they have been shown to model temporal correlations
successfully as well. These models provide several benefits over other types of
generative models due to their training stability, invertibility and efficiency
in sampling and inference. This makes them a suitable candidate for stochastic
spatio-temporal prediction problems, which are omnipresent in many fields of
sciences, such as earth sciences, astrophysics or molecular sciences. In this
paper, we present conditional normalizing flows for stochastic spatio-temporal
modelling. The method is evaluated on the task of daily temperature and hourly
geopotential map prediction from ERA5 datasets. Experiments show that our
method is able to capture spatio-temporal correlations and extrapolates well
beyond the time horizon used during training.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Unmasking Deepfake Faces from Videos Using An Explainable Cost-Sensitive Deep Learning Approach
Abstract: Deepfake technology is widely used, which has led to serious worries about
the authenticity of digital media, making the need for trustworthy deepfake
face recognition techniques more urgent than ever. This study employs a
resource-effective and transparent cost-sensitive deep learning method to
effectively detect deepfake faces in videos. To create a reliable deepfake
detection system, four pre-trained Convolutional Neural Network (CNN) models:
XceptionNet, InceptionResNetV2, EfficientNetV2S, and EfficientNetV2M were used.
FaceForensics++ and CelebDf-V2 as benchmark datasets were used to assess the
performance of our method. To efficiently process video data, key frame
extraction was used as a feature extraction technique. Our main contribution is
to show the models adaptability and effectiveness in correctly identifying
deepfake faces in videos. Furthermore, a cost-sensitive neural network method
was applied to solve the dataset imbalance issue that arises frequently in
deepfake detection. The XceptionNet model on the CelebDf-V2 dataset gave the
proposed methodology a 98% accuracy, which was the highest possible whereas,
the InceptionResNetV2 model, achieves an accuracy of 94% on the FaceForensics++
dataset. Source Code:
https://github.com/Faysal-MD/Unmasking-Deepfake-Faces-from-Videos-An-Explainable-Cost-Sensitive-Deep-Learning-Approach-IEEE2023

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Moderating Model Marketplaces: Platform Governance Puzzles for AI Intermediaries
Abstract: The AI development community is increasingly making use of hosting
intermediaries such as Hugging Face provide easy access to user-uploaded models
and training data. These model marketplaces lower technical deployment barriers
for hundreds of thousands of users, yet can be used in numerous potentially
harmful and illegal ways. In this article, we explain ways in which AI systems,
which can both `contain' content and be open-ended tools, present one of the
trickiest platform governance challenges seen to date. We provide case studies
of several incidents across three illustrative platforms -- Hugging Face,
GitHub and Civitai -- to examine how model marketplaces moderate models.
Building on this analysis, we outline important (and yet nevertheless limited)
practices that industry has been developing to respond to moderation demands:
licensing, access and use restrictions, automated content moderation, and open
policy development. While the policy challenge at hand is a considerable one,
we conclude with some ideas as to how platforms could better mobilize resources
to act as a careful, fair, and proportionate regulatory access point.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis
Abstract: Building general-purpose robots that can operate seamlessly, in any
environment, with any object, and utilizing various skills to complete diverse
tasks has been a long-standing goal in Artificial Intelligence. Unfortunately,
however, most existing robotic systems have been constrained - having been
designed for specific tasks, trained on specific datasets, and deployed within
specific environments. These systems usually require extensively-labeled data,
rely on task-specific models, have numerous generalization issues when deployed
in real-world scenarios, and struggle to remain robust to distribution shifts.
Motivated by the impressive open-set performance and content generation
capabilities of web-scale, large-capacity pre-trained models (i.e., foundation
models) in research fields such as Natural Language Processing (NLP) and
Computer Vision (CV), we devote this survey to exploring (i) how these existing
foundation models from NLP and CV can be applied to the field of robotics, and
also exploring (ii) what a robotics-specific foundation model would look like.
We begin by providing an overview of what constitutes a conventional robotic
system and the fundamental barriers to making it universally applicable. Next,
we establish a taxonomy to discuss current work exploring ways to leverage
existing foundation models for robotics and develop ones catered to robotics.
Finally, we discuss key challenges and promising future directions in using
foundation models for enabling general-purpose robotic systems. We encourage
readers to view our living GitHub repository of resources, including papers
reviewed in this survey as well as related projects and repositories for
developing foundation models for robotics.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: InstructPTS: Instruction-Tuning LLMs for Product Title Summarization
Abstract: E-commerce product catalogs contain billions of items. Most products have
lengthy titles, as sellers pack them with product attributes to improve
retrieval, and highlight key product aspects. This results in a gap between
such unnatural products titles, and how customers refer to them. It also limits
how e-commerce stores can use these seller-provided titles for recommendation,
QA, or review summarization.
  Inspired by recent work on instruction-tuned LLMs, we present InstructPTS, a
controllable approach for the task of Product Title Summarization (PTS).
Trained using a novel instruction fine-tuning strategy, our approach is able to
summarize product titles according to various criteria (e.g. number of words in
a summary, inclusion of specific phrases, etc.). Extensive evaluation on a
real-world e-commerce catalog shows that compared to simple fine-tuning of
LLMs, our proposed approach can generate more accurate product name summaries,
with an improvement of over 14 and 8 BLEU and ROUGE points, respectively.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Will releasing the weights of future large language models grant widespread access to pandemic agents?
Abstract: Large language models can benefit research and human understanding by
providing tutorials that draw on expertise from many different fields. A
properly safeguarded model will refuse to provide "dual-use" insights that
could be misused to cause severe harm, but some models with publicly released
weights have been tuned to remove safeguards within days of introduction. Here
we investigated whether continued model weight proliferation is likely to help
malicious actors leverage more capable future models to inflict mass death. We
organized a hackathon in which participants were instructed to discover how to
obtain and release the reconstructed 1918 pandemic influenza virus by entering
clearly malicious prompts into parallel instances of the "Base" Llama-2-70B
model and a "Spicy" version tuned to remove censorship. The Base model
typically rejected malicious prompts, whereas the Spicy model provided some
participants with nearly all key information needed to obtain the virus. Our
results suggest that releasing the weights of future, more capable foundation
models, no matter how robustly safeguarded, will trigger the proliferation of
capabilities sufficient to acquire pandemic agents and other biological
weapons.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Data-Efficient Alignment of Large Language Models with Human Feedback Through Natural Language
Abstract: Learning from human feedback is a prominent technique to align the output of
large language models (LLMs) with human expectations. Reinforcement learning
from human feedback (RLHF) leverages human preference signals that are in the
form of ranking of response pairs to perform this alignment. However, human
preference on LLM outputs can come in much richer forms including natural
language, which may provide detailed feedback on strengths and weaknesses of a
given response. In this work we investigate data efficiency of modeling human
feedback that is in natural language. Specifically, we fine-tune an open-source
LLM, e.g., Falcon-40B-Instruct, on a relatively small amount (1000 records or
even less) of human feedback in natural language in the form of critiques and
revisions of responses. We show that this model is able to improve the quality
of responses from even some of the strongest LLMs such as ChatGPT, BARD, and
Vicuna, through critique and revision of those responses. For instance, through
one iteration of revision of ChatGPT responses, the revised responses have
56.6% win rate over the original ones, and this win rate can be further
improved to 65.9% after applying the revision for five iterations.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Two Stream Scene Understanding on Graph Embedding
Abstract: The paper presents a novel two-stream network architecture for enhancing
scene understanding in computer vision. This architecture utilizes a graph
feature stream and an image feature stream, aiming to merge the strengths of
both modalities for improved performance in image classification and scene
graph generation tasks. The graph feature stream network comprises a
segmentation structure, scene graph generation, and a graph representation
module. The segmentation structure employs the UPSNet architecture with a
backbone that can be a residual network, Vit, or Swin Transformer. The scene
graph generation component focuses on extracting object labels and neighborhood
relationships from the semantic map to create a scene graph. Graph
Convolutional Networks (GCN), GraphSAGE, and Graph Attention Networks (GAT) are
employed for graph representation, with an emphasis on capturing node features
and their interconnections. The image feature stream network, on the other
hand, focuses on image classification through the use of Vision Transformer and
Swin Transformer models. The two streams are fused using various data fusion
methods. This fusion is designed to leverage the complementary strengths of
graph-based and image-based features.Experiments conducted on the ADE20K
dataset demonstrate the effectiveness of the proposed two-stream network in
improving image classification accuracy compared to conventional methods. This
research provides a significant contribution to the field of computer vision,
particularly in the areas of scene understanding and image classification, by
effectively combining graph-based and image-based approaches.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Perspectives on the State and Future of Deep Learning -- 2023
Abstract: The goal of this series is to chronicle opinions and issues in the field of
machine learning as they stand today and as they change over time. The plan is
to host this survey periodically until the AI singularity
paperclip-frenzy-driven doomsday, keeping an updated list of topical questions
and interviewing new community members for each edition. In this issue, we
probed people's opinions on interpretable AI, the value of benchmarking in
modern NLP, the state of progress towards understanding deep learning, and the
future of academia.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans
Abstract: Diffusion-based planning has shown promising results in long-horizon,
sparse-reward tasks by training trajectory diffusion models and conditioning
the sampled trajectories using auxiliary guidance functions. However, due to
their nature as generative models, diffusion models are not guaranteed to
generate feasible plans, resulting in failed execution and precluding planners
from being useful in safety-critical applications. In this work, we propose a
novel approach to refine unreliable plans generated by diffusion models by
providing refining guidance to error-prone plans. To this end, we suggest a new
metric named restoration gap for evaluating the quality of individual plans
generated by the diffusion model. A restoration gap is estimated by a gap
predictor which produces restoration gap guidance to refine a diffusion
planner. We additionally present an attribution map regularizer to prevent
adversarial refining guidance that could be generated from the sub-optimal gap
predictor, which enables further refinement of infeasible plans. We demonstrate
the effectiveness of our approach on three different benchmarks in offline
control settings that require long-horizon planning. We also illustrate that
our approach presents explainability by presenting the attribution maps of the
gap predictor and highlighting error-prone transitions, allowing for a deeper
understanding of the generated plans.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning
Abstract: Federated learning is a technique of decentralized machine learning. that
allows multiple parties to collaborate and learn a shared model without sharing
their raw data. Our paper proposes a federated learning framework for intrusion
detection in Internet of Vehicles (IOVs) using the CIC-IDS 2017 dataset. The
proposed framework employs SMOTE for handling class imbalance, outlier
detection for identifying and removing abnormal observations, and
hyperparameter tuning to optimize the model's performance. The authors
evaluated the proposed framework using various performance metrics and
demonstrated its effectiveness in detecting intrusions with other datasets
(KDD-Cup 99 and UNSW- NB-15) and conventional classifiers. Furthermore, the
proposed framework can protect sensitive data while achieving high intrusion
detection performance.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Data-Driven Traffic Reconstruction and Kernel Methods for Identifying Stop-and-Go Congestion
Abstract: Identifying stop-and-go events (SAGs) in traffic flow presents an important
avenue for advancing data-driven research for climate change mitigation and
sustainability, owing to their substantial impact on carbon emissions, travel
time, fuel consumption, and roadway safety. In fact, SAGs are estimated to
account for 33-50% of highway driving externalities. However, insufficient
attention has been paid to precisely quantifying where, when, and how much
these SAGs take place -necessary for downstream decision making, such as
intervention design and policy analysis. A key challenge is that the data
available to researchers and governments are typically sparse and aggregated to
a granularity that obscures SAGs. To overcome such data limitations, this study
thus explores the use of traffic reconstruction techniques for SAG
identification. In particular, we introduce a kernel-based method for
identifying spatio-temporal features in traffic and leverage bootstrapping to
quantify the uncertainty of the reconstruction process. Experimental results on
California highway data demonstrate the promise of the method for capturing
SAGs. This work contributes to a foundation for data-driven decision making to
advance sustainability of traffic systems.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Generalization Analysis of Policy Networks: An Example of Double-Integrator
Abstract: Extensive utilization of deep reinforcement learning (DRL) policy networks in
diverse continuous control tasks has raised questions regarding performance
degradation in expansive state spaces where the input state norm is larger than
that in the training environment. This paper aims to uncover the underlying
factors contributing to such performance deterioration when dealing with
expanded state spaces, using a novel analysis technique known as state
division. In contrast to prior approaches that employ state division merely as
a post-hoc explanatory tool, our methodology delves into the intrinsic
characteristics of DRL policy networks. Specifically, we demonstrate that the
expansion of state space induces the activation function $\tanh$ to exhibit
saturability, resulting in the transformation of the state division boundary
from nonlinear to linear. Our analysis centers on the paradigm of the
double-integrator system, revealing that this gradual shift towards linearity
imparts a control behavior reminiscent of bang-bang control. However, the
inherent linearity of the division boundary prevents the attainment of an ideal
bang-bang control, thereby introducing unavoidable overshooting. Our
experimental investigations, employing diverse RL algorithms, establish that
this performance phenomenon stems from inherent attributes of the DRL policy
network, remaining consistent across various optimization algorithms.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Using Slisemap to interpret physical data
Abstract: Manifold visualisation techniques are commonly used to visualise
high-dimensional datasets in physical sciences. In this paper we apply a
recently introduced manifold visualisation method, called Slise, on datasets
from physics and chemistry. Slisemap combines manifold visualisation with
explainable artificial intelligence. Explainable artificial intelligence is
used to investigate the decision processes of black box machine learning models
and complex simulators. With Slisemap we find an embedding such that data items
with similar local explanations are grouped together. Hence, Slisemap gives us
an overview of the different behaviours of a black box model. This makes
Slisemap into a supervised manifold visualisation method, where the patterns in
the embedding reflect a target property. In this paper we show how Slisemap can
be used and evaluated on physical data and that Slisemap is helpful in finding
meaningful information on classification and regression models trained on these
datasets.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Impact of HPO on AutoML Forecasting Ensembles
Abstract: A forecasting ensemble consisting of a diverse range of estimators for both
local and global univariate forecasting, in particular MQ-CNN,DeepAR, Prophet,
NPTS, ARIMA and ETS, can be used to make forecasts for a variety of problems.
This paper delves into the aspect of adding different hyperparameter
optimization strategies to the deep learning models in such a setup (DeepAR and
MQ-CNN), exploring the trade-off between added training cost and the increase
in accuracy for different configurations. It shows that in such a setup, adding
hyperparameter optimization can lead to performance improvements, with the
final setup having a 9.9 % percent accuracy improvement with respect to the
avg-wQL over the baseline ensemble without HPO, accompanied by a 65.8 %
increase in end-to-end ensemble latency. This improvement is based on an
empirical analysis of combining the ensemble pipeline with different tuning
strategies, namely Bayesian Optimisation and Hyperband and different
configurations of those strategies. In the final configuration, the proposed
combination of ensemble learning and HPO outperforms the state of the art
commercial AutoML forecasting solution, Amazon Forecast, with a 3.5 % lower
error and 16.0 % lower end-to-end ensemble latency.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach
Abstract: The integration of machine learning (ML) into cyber-physical systems (CPS)
offers significant benefits, including enhanced efficiency, predictive
capabilities, real-time responsiveness, and the enabling of autonomous
operations. This convergence has accelerated the development and deployment of
a range of real-world applications, such as autonomous vehicles, delivery
drones, service robots, and telemedicine procedures. However, the software
development life cycle (SDLC) for AI-infused CPS diverges significantly from
traditional approaches, featuring data and learning as two critical components.
Existing verification and validation techniques are often inadequate for these
new paradigms. In this study, we pinpoint the main challenges in ensuring
formal safety for learningenabled CPS.We begin by examining testing as the most
pragmatic method for verification and validation, summarizing the current
state-of-the-art methodologies. Recognizing the limitations in current testing
approaches to provide formal safety guarantees, we propose a roadmap to
transition from foundational probabilistic testing to a more rigorous approach
capable of delivering formal assurance.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Embarassingly Simple Dataset Distillation
Abstract: Dataset distillation extracts a small set of synthetic training samples from
a large dataset with the goal of achieving competitive performance on test data
when trained on this sample. In this work, we tackle dataset distillation at
its core by treating it directly as a bilevel optimization problem.
Re-examining the foundational back-propagation through time method, we study
the pronounced variance in the gradients, computational burden, and long-term
dependencies. We introduce an improved method: Random Truncated Backpropagation
Through Time (RaT-BPTT) to address them. RaT-BPTT incorporates a truncation
coupled with a random window, effectively stabilizing the gradients and
speeding up the optimization while covering long dependencies. This allows us
to establish new state-of-the-art for a variety of standard dataset benchmarks.
A deeper dive into the nature of distilled data unveils pronounced
intercorrelation. In particular, subsets of distilled datasets tend to exhibit
much worse performance than directly distilled smaller datasets of the same
size. Leveraging RaT-BPTT, we devise a boosting mechanism that generates
distilled datasets that contain subsets with near optimal performance across
different data budgets.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: VideoAssembler: Identity-Consistent Video Generation with Reference Entities using Diffusion Model
Abstract: Identity-consistent video generation seeks to synthesize videos that are
guided by both textual prompts and reference images of entities. Current
approaches typically utilize cross-attention layers to integrate the appearance
of the entity, which predominantly captures semantic attributes, resulting in
compromised fidelity of entities. Moreover, these methods necessitate iterative
fine-tuning for each new entity encountered, thereby limiting their
applicability. To address these challenges, we introduce VideoAssembler, a
novel end-to-end framework for identity-consistent video generation that can
conduct inference directly when encountering new entities. VideoAssembler is
adept at producing videos that are not only flexible with respect to the input
reference entities but also responsive to textual conditions. Additionally, by
modulating the quantity of input images for the entity, VideoAssembler enables
the execution of tasks ranging from image-to-video generation to sophisticated
video editing. VideoAssembler comprises two principal components: the Reference
Entity Pyramid (REP) encoder and the Entity-Prompt Attention Fusion (EPAF)
module. The REP encoder is designed to infuse comprehensive appearance details
into the denoising stages of the stable diffusion model. Concurrently, the EPAF
module is utilized to integrate text-aligned features effectively. Furthermore,
to mitigate the challenge of scarce data, we present a methodology for the
preprocessing of training data. Our evaluation of the VideoAssembler framework
on the UCF-101, MSR-VTT, and DAVIS datasets indicates that it achieves good
performances in both quantitative and qualitative analyses (346.84 in FVD and
48.01 in IS on UCF-101). Our project page is at
https://gulucaptain.github.io/videoassembler/.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Improving fit to human reading times via temperature-scaled surprisal
Abstract: Past studies have provided broad support for that words with lower
predictability (i.e., higher surprisal) require more time for comprehension by
using large language models (LLMs) to simulate humans' cognitive load. In
general, these studies have implicitly assumed that the probability scores from
LLMs are accurate, ignoring the discrepancies between human cognition and LLMs
from this standpoint. Inspired by the concept of probability calibration, we
are the first work to focus on the probability distribution for human reading
simulation. We propose to use temperature-scaled surprisal, a surprisal
calculated by shaped probability, to be the predictor of human reading times.
Our results across three corpora consistently revealed that such a surprisal
can drastically improve the prediction of reading times. Setting the
temperature to be approximately 2.5 across all models and datasets can yield up
to an 89% of increase in delta log-likelihood in our setting. We also propose a
calibration metric to quantify the possible human-likeness bias. Further
analysis was done and provided insights into this phenomenon.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency Training
Abstract: Multimodal reasoning is a challenging task that requires models to reason
across multiple modalities to answer questions. Existing approaches have made
progress by incorporating language and visual modalities into a two-stage
reasoning framework, separating rationale generation from answer inference.
However, these approaches often fall short due to the inadequate quality of the
generated rationales. In this work, we delve into the importance of rationales
in model reasoning. We observe that when rationales are completely accurate,
the model's accuracy significantly improves, highlighting the need for
high-quality rationale generation. Motivated by this, we propose MC-CoT, a
self-consistency training strategy that generates multiple rationales and
answers, subsequently selecting the most accurate through a voting process.
This approach not only enhances the quality of generated rationales but also
leads to more accurate and robust answers. Through extensive experiments, we
demonstrate that our approach significantly improves model performance across
various benchmarks. Remarkably, we show that even smaller base models, when
equipped with our proposed approach, can achieve results comparable to those of
larger models, illustrating the potential of our approach in harnessing the
power of rationales for improved multimodal reasoning. The code is available at
https://github.com/chengtan9907/mc-cot.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Effective Backdoor Mitigation Depends on the Pre-training Objective
Abstract: Despite the advanced capabilities of contemporary machine learning (ML)
models, they remain vulnerable to adversarial and backdoor attacks. This
vulnerability is particularly concerning in real-world deployments, where
compromised models may exhibit unpredictable behavior in critical scenarios.
Such risks are heightened by the prevalent practice of collecting massive,
internet-sourced datasets for pre-training multimodal models, as these datasets
may harbor backdoors. Various techniques have been proposed to mitigate the
effects of backdooring in these models such as CleanCLIP which is the current
state-of-the-art approach. In this work, we demonstrate that the efficacy of
CleanCLIP in mitigating backdoors is highly dependent on the particular
objective used during model pre-training. We observe that stronger pre-training
objectives correlate with harder to remove backdoors behaviors. We show this by
training multimodal models on two large datasets consisting of 3 million (CC3M)
and 6 million (CC6M) datapoints, under various pre-training objectives,
followed by poison removal using CleanCLIP. We find that CleanCLIP is
ineffective when stronger pre-training objectives are used, even with extensive
hyperparameter tuning. Our findings underscore critical considerations for ML
practitioners who pre-train models using large-scale web-curated data and are
concerned about potential backdoor threats. Notably, our results suggest that
simpler pre-training objectives are more amenable to effective backdoor
removal. This insight is pivotal for practitioners seeking to balance the
trade-offs between using stronger pre-training objectives and security against
backdoor attacks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Fact-based Court Judgment Prediction
Abstract: This extended abstract extends the research presented in "ILDC for CJPE:
Indian Legal Documents Corpus for Court Judgment Prediction and Explanation"
\cite{malik-etal-2021-ildc}, focusing on fact-based judgment prediction within
the context of Indian legal documents. We introduce two distinct problem
variations: one based solely on facts, and another combining facts with rulings
from lower courts (RLC). Our research aims to enhance early-phase case outcome
prediction, offering significant benefits to legal professionals and the
general public. The results, however, indicated a performance decline compared
to the original ILDC for CJPE study, even after implementing various weightage
schemes in our DELSumm algorithm. Additionally, using only facts for legal
judgment prediction with different transformer models yielded results inferior
to the state-of-the-art outcomes reported in the "ILDC for CJPE" study.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Act-VIT: A Representationally Robust Attention Architecture for Skeleton Based Action Recognition Using Vision Transformer
Abstract: Skeleton-based action recognition receives the attention of many researchers
as it is robust to viewpoint and illumination changes, and its processing is
much more efficient than video frames. With the emergence of deep learning
models, it has become very popular to represent the skeleton data in
pseudo-image form and apply Convolutional Neural Networks for action
recognition. Thereafter, studies concentrated on finding effective methods for
forming pseudo-images. Recently, attention networks, more specifically
transformers have provided promising results in various vision problems. In
this study, the effectiveness of vision transformers for skeleton-based action
recognition is examined and its robustness on the pseudo-image representation
scheme is investigated. To this end, a three-level architecture, Act-VIT is
proposed, which forms a set of pseudo images apply a classifier on each of the
representation and combine their results to find the final action class. The
classifiers of Act-VIT are first realized by CNNs and then by VITs and their
performances are compared. Experimental studies reveal that the vision
transformer is less sensitive to the initial pseudo-image representation
compared to CNN. Nevertheless, even with the vision transformer, the
recognition performance can be further improved by consensus of classifiers.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Video Summarization: Towards Entity-Aware Captions
Abstract: Existing popular video captioning benchmarks and models deal with generic
captions devoid of specific person, place or organization named entities. In
contrast, news videos present a challenging setting where the caption requires
such named entities for meaningful summarization. As such, we propose the task
of summarizing news video directly to entity-aware captions. We also release a
large-scale dataset, VIEWS (VIdeo NEWS), to support research on this task.
Further, we propose a method that augments visual information from videos with
context retrieved from external world knowledge to generate entity-aware
captions. We demonstrate the effectiveness of our approach on three video
captioning models. We also show that our approach generalizes to existing news
image captions dataset. With all the extensive experiments and insights, we
believe we establish a solid basis for future research on this challenging
task.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Overview of the TREC 2023 Product Product Search Track
Abstract: This is the first year of the TREC Product search track. The focus this year
was the creation of a reusable collection and evaluation of the impact of the
use of metadata and multi-modal data on retrieval accuracy. This year we
leverage the new product search corpus, which includes contextual metadata. Our
analysis shows that in the product search domain, traditional retrieval systems
are highly effective and commonly outperform general-purpose pretrained
embedding models. Our analysis also evaluates the impact of using simplified
and metadata-enhanced collections, finding no clear trend in the impact of the
expanded collection. We also see some surprising outcomes; despite their
widespread adoption and competitive performance on other tasks, we find
single-stage dense retrieval runs can commonly be noncompetitive or generate
low-quality results both in the zero-shot and fine-tuned domain.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees
Abstract: This tutorial focuses on efficient methods to predictive monitoring (PM), the
problem of detecting at runtime future violations of a given requirement from
the current state of a system. While performing model checking at runtime would
offer a precise solution to the PM problem, it is generally computationally
expensive. To address this scalability issue, several lightweight approaches
based on machine learning have recently been proposed. These approaches work by
learning an approximate yet efficient surrogate (deep learning) model of the
expensive model checker. A key challenge remains to ensure reliable
predictions, especially in safety-critical applications. We review our recent
work on predictive monitoring, one of the first to propose learning-based
approximations for CPS verification of temporal logic specifications and the
first in this context to apply conformal prediction (CP) for rigorous
uncertainty quantification. These CP-based uncertainty estimators offer
statistical guarantees regarding the generalization error of the learning
model, and they can be used to determine unreliable predictions that should be
rejected. In this tutorial, we present a general and comprehensive framework
summarizing our approach to the predictive monitoring of CPSs, examining in
detail several variants determined by three main dimensions: system dynamics
(deterministic, non-deterministic, stochastic), state observability, and
semantics of requirements' satisfaction (Boolean or quantitative).

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Mark My Words: Analyzing and Evaluating Language Model Watermarks
Abstract: The capabilities of large language models have grown significantly in recent
years and so too have concerns about their misuse. In this context, the ability
to distinguish machine-generated text from human-authored content becomes
important. Prior works have proposed numerous schemes to watermark text, which
would benefit from a systematic evaluation framework. This work focuses on text
watermarking techniques - as opposed to image watermarks - and proposes
MARKMYWORDS, a comprehensive benchmark for them under different tasks as well
as practical attacks. We focus on three main metrics: quality, size (e.g. the
number of tokens needed to detect a watermark), and tamper-resistance. Current
watermarking techniques are good enough to be deployed: Kirchenbauer et al. [1]
can watermark Llama2-7B-chat with no perceivable loss in quality, the watermark
can be detected with fewer than 100 tokens, and the scheme offers good
tamper-resistance to simple attacks. We argue that watermark
indistinguishability, a criteria emphasized in some prior works, is too strong
a requirement: schemes that slightly modify logit distributions outperform
their indistinguishable counterparts with no noticeable loss in generation
quality. We publicly release our benchmark
(https://github.com/wagner-group/MarkMyWords)

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Inversion-Free Image Editing with Natural Language
Abstract: Despite recent advances in inversion-based editing, text-guided image
manipulation remains challenging for diffusion models. The primary bottlenecks
include 1) the time-consuming nature of the inversion process; 2) the struggle
to balance consistency with accuracy; 3) the lack of compatibility with
efficient consistency sampling methods used in consistency models. To address
the above issues, we start by asking ourselves if the inversion process can be
eliminated for editing. We show that when the initial sample is known, a
special variance schedule reduces the denoising step to the same form as the
multi-step consistency sampling. We name this Denoising Diffusion Consistent
Model (DDCM), and note that it implies a virtual inversion strategy without
explicit inversion in sampling. We further unify the attention control
mechanisms in a tuning-free framework for text-guided editing. Combining them,
we present inversion-free editing (InfEdit), which allows for consistent and
faithful editing for both rigid and non-rigid semantic changes, catering to
intricate modifications without compromising on the image's integrity and
explicit inversion. Through extensive experiments, InfEdit shows strong
performance in various editing tasks and also maintains a seamless workflow
(less than 3 seconds on one single A40), demonstrating the potential for
real-time applications. Project Page: https://sled-group.github.io/InfEdit/

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Social Contract AI: Aligning AI Assistants with Implicit Group Norms
Abstract: We explore the idea of aligning an AI assistant by inverting a model of
users' (unknown) preferences from observed interactions. To validate our
proposal, we run proof-of-concept simulations in the economic ultimatum game,
formalizing user preferences as policies that guide the actions of simulated
players. We find that the AI assistant accurately aligns its behavior to match
standard policies from the economic literature (e.g., selfish, altruistic).
However, the assistant's learned policies lack robustness and exhibit limited
generalization in an out-of-distribution setting when confronted with a
currency (e.g., grams of medicine) that was not included in the assistant's
training distribution. Additionally, we find that when there is inconsistency
in the relationship between language use and an unknown policy (e.g., an
altruistic policy combined with rude language), the assistant's learning of the
policy is slowed. Overall, our preliminary results suggest that developing
simulation frameworks in which AI assistants need to infer preferences from
diverse users can provide a valuable approach for studying practical alignment
questions.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Enchancing Semi-Supervised Learning for Extractive Summarization with an LLM-based pseudolabeler
Abstract: This work tackles the task of extractive text summarization in a limited
labeled data scenario using a semi-supervised approach. Specifically, we
propose a prompt-based pseudolabel selection strategy using GPT-4. We evaluate
our method on three text summarization datasets: TweetSumm, WikiHow, and
ArXiv/PubMed. Our experiments show that by using an LLM to evaluate and
generate pseudolabels, we can improve the ROUGE-1 by 10-20\% on the different
datasets, which is akin to enhancing pretrained models. We also show that such
a method needs a smaller pool of unlabeled examples to perform better.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Can we infer the presence of Differential Privacy in Deep Learning models' weights? Towards more secure Deep Learning
Abstract: Differential Privacy (DP) is a key property to protect data and models from
integrity attacks. In the Deep Learning (DL) field, it is commonly implemented
through the Differentially Private Stochastic Gradient Descent (DP-SGD).
However, when a model is shared or released, there is no way to check whether
it is differentially private, that is, it required to trust the model provider.
This situation poses a problem when data privacy is mandatory, specially with
current data regulations, as the presence of DP can not be certificated
consistently by any third party. Thus, we face the challenge of determining
whether a DL model has been trained with DP, according to the title question:
Can we infer the presence of Differential Privacy in Deep Learning models'
weights? Since the DP-SGD significantly changes the training process of a DL
model, we hypothesize that DP leaves an imprint in the weights of a DL model,
which can be used to predict whether a model has been trained with DP
regardless of its architecture and the training dataset. In this paper, we
propose to employ the imprint in model weights of using DP to infer the
presence of DP training in a DL model. To substantiate our hypothesis, we
developed an experimental methodology based on two datasets of weights of DL
models, each with models with and without DP training and a meta-classifier to
infer whether DP was used in the training process of a DL model, by accessing
its weights. We accomplish both, the removal of the requirement of a trusted
model provider and a strong foundation for this interesting line of research.
Thus, our contribution is an additional layer of security on top of the strict
private requirements of DP training in DL models, towards to DL models.

Output: Machine Learning