Instruction: What field is the article from?

Input:
Title: Enhancing Logical Reasoning in Large Language Models to Facilitate Legal Applications
Abstract: Language serves as a vehicle for conveying thought, enabling communication
among individuals. The ability to distinguish between diverse concepts,
identify fairness and injustice, and comprehend a range of legal notions
fundamentally relies on logical reasoning. Large Language Models (LLMs) attempt
to emulate human language understanding and generation, but their competency in
logical reasoning remains limited. This paper seeks to address the
philosophical question: How can we effectively teach logical reasoning to LLMs
while maintaining a deep understanding of the intricate relationship between
language and logic? By focusing on bolstering LLMs' capabilities in logical
reasoning, we aim to expand their applicability in law and other
logic-intensive disciplines. To this end, we propose a Reinforcement Learning
from Logical Feedback (RLLF) approach, which serves as a potential framework
for refining LLMs' reasoning capacities. Through RLLF and a revised evaluation
methodology, we explore new avenues for research in this domain and contribute
to the development of LLMs capable of handling complex legal reasoning tasks
while acknowledging the fundamental connection between language and logic.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Potato Leaf Disease Classification using Deep Learning: A Convolutional Neural Network Approach
Abstract: In this study, a Convolutional Neural Network (CNN) is used to classify
potato leaf illnesses using Deep Learning. The suggested approach entails
preprocessing the leaf image data, training a CNN model on that data, and
assessing the model's success on a test set. The experimental findings show
that the CNN model, with an overall accuracy of 99.1%, is highly accurate in
identifying two kinds of potato leaf diseases, including Early Blight, Late
Blight, and Healthy. The suggested method may offer a trustworthy and effective
remedy for identifying potato diseases, which is essential for maintaining food
security and minimizing financial losses in agriculture. The model can
accurately recognize the various disease types even when there are severe
infections present. This work highlights the potential of deep learning methods
for categorizing potato diseases, which can help with effective and automated
disease management in potato farming.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Human-Centered Planning
Abstract: LLMs have recently made impressive inroads on tasks whose output is
structured, such as coding, robotic planning and querying databases. The vision
of creating AI-powered personal assistants also involves creating structured
outputs, such as a plan for one's day, or for an overseas trip. Here, since the
plan is executed by a human, the output doesn't have to satisfy strict
syntactic constraints. A useful assistant should also be able to incorporate
vague constraints specified by the user in natural language. This makes LLMs an
attractive option for planning.
  We consider the problem of planning one's day. We develop an LLM-based
planner (LLMPlan) extended with the ability to self-reflect on its output and a
symbolic planner (SymPlan) with the ability to translate text constraints into
a symbolic representation. Despite no formal specification of constraints, we
find that LLMPlan performs explicit constraint satisfaction akin to the
traditional symbolic planners on average (2% performance difference), while
retaining the reasoning of implicit requirements. Consequently, LLM-based
planners outperform their symbolic counterparts in user satisfaction (70.5% vs.
40.4%) during interactive evaluation with 40 users.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: UFPS: A unified framework for partially-annotated federated segmentation in heterogeneous data distribution
Abstract: Partially supervised segmentation is a label-saving method based on datasets
with fractional classes labeled and intersectant. However, it is still far from
landing on real-world medical applications due to privacy concerns and data
heterogeneity. As a remedy without privacy leakage, federated partially
supervised segmentation (FPSS) is formulated in this work. The main challenges
for FPSS are class heterogeneity and client drift. We propose a Unified
Federated Partially-labeled Segmentation (UFPS) framework to segment pixels
within all classes for partially-annotated datasets by training a totipotential
global model without class collision. Our framework includes Unified Label
Learning and sparsed Unified Sharpness Aware Minimization for unification of
class and feature space, respectively. We find that vanilla combinations for
traditional methods in partially supervised segmentation and federated learning
are mainly hampered by class collision through empirical study. Our
comprehensive experiments on real medical datasets demonstrate better
deconflicting and generalization ability of UFPS compared with modified
methods.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Symbolic Numeric Planning with Patterns
Abstract: In this paper, we propose a novel approach for solving linear numeric
planning problems, called Symbolic Pattern Planning. Given a planning problem
$\Pi$, a bound $n$ and a pattern -- defined as an arbitrary sequence of actions
-- we encode the problem of finding a plan for $\Pi$ with bound $n$ as a
formula with fewer variables and/or clauses than the state-of-the-art rolled-up
and relaxed-relaxed-$\exists$ encodings. More importantly, we prove that for
any given bound, it is never the case that the latter two encodings allow
finding a valid plan while ours does not. On the experimental side, we consider
6 other planning systems -- including the ones which participated in this
year's International Planning Competition (IPC) -- and we show that our planner
Patty has remarkably good comparative performances on this year's IPC problems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain
Abstract: The previous state-of-the-art (SOTA) method achieved a remarkable execution
accuracy on the Spider dataset, which is one of the largest and most diverse
datasets in the Text-to-SQL domain. However, during our reproduction of the
business dataset, we observed a significant drop in performance. We examined
the differences in dataset complexity, as well as the clarity of questions'
intentions, and assessed how those differences could impact the performance of
prompting methods. Subsequently, We develop a more adaptable and more general
prompting method, involving mainly query rewriting and SQL boosting, which
respectively transform vague information into exact and precise information and
enhance the SQL itself by incorporating execution feedback and the query
results from the database content. In order to prevent information gaps, we
include the comments, value types, and value samples for columns as part of the
database description in the prompt. Our experiments with Large Language Models
(LLMs) illustrate the significant performance improvement on the business
dataset and prove the substantial potential of our method. In terms of
execution accuracy on the business dataset, the SOTA method scored 21.05, while
our approach scored 65.79. As a result, our approach achieved a notable
performance improvement even when using a less capable pre-trained language
model. Last but not least, we also explore the Text-to-Python and
Text-to-Function options, and we deeply analyze the pros and cons among them,
offering valuable insights to the community.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Attribute Annotation and Bias Evaluation in Visual Datasets for Autonomous Driving
Abstract: This paper addresses the often overlooked issue of fairness in the autonomous
driving domain, particularly in vision-based perception and prediction systems,
which play a pivotal role in the overall functioning of Autonomous Vehicles
(AVs). We focus our analysis on biases present in some of the most commonly
used visual datasets for training person and vehicle detection systems. We
introduce an annotation methodology and a specialised annotation tool, both
designed to annotate protected attributes of agents in visual datasets. We
validate our methodology through an inter-rater agreement analysis and provide
the distribution of attributes across all datasets. These include annotations
for the attributes age, sex, skin tone, group, and means of transport for more
than 90K people, as well as vehicle type, colour, and car type for over 50K
vehicles. Generally, diversity is very low for most attributes, with some
groups, such as children, wheelchair users, or personal mobility vehicle users,
being extremely underrepresented in the analysed datasets. The study
contributes significantly to efforts to consider fairness in the evaluation of
perception and prediction systems for AVs. This paper follows reproducibility
principles. The annotation tool, scripts and the annotated attributes can be
accessed publicly at https://github.com/ec-jrc/humaint_annotator.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Vision-Language Interpreter for Robot Task Planning
Abstract: Large language models (LLMs) are accelerating the development of
language-guided robot planners. Meanwhile, symbolic planners offer the
advantage of interpretability. This paper proposes a new task that bridges
these two trends, namely, multimodal planning problem specification. The aim is
to generate a problem description (PD), a machine-readable file used by the
planners to find a plan. By generating PDs from language instruction and scene
observation, we can drive symbolic planners in a language-guided framework. We
propose a Vision-Language Interpreter (ViLaIn), a new framework that generates
PDs using state-of-the-art LLM and vision-language models. ViLaIn can refine
generated PDs via error message feedback from the symbolic planner. Our aim is
to answer the question: How accurately can ViLaIn and the symbolic planner
generate valid robot plans? To evaluate ViLaIn, we introduce a novel dataset
called the problem description generation (ProDG) dataset. The framework is
evaluated with four new evaluation metrics. Experimental results show that
ViLaIn can generate syntactically correct problems with more than 99% accuracy
and valid plans with more than 58% accuracy.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: MOSEL: Inference Serving Using Dynamic Modality Selection
Abstract: Rapid advancements over the years have helped machine learning models reach
previously hard-to-achieve goals, sometimes even exceeding human capabilities.
However, to attain the desired accuracy, the model sizes and in turn their
computational requirements have increased drastically. Thus, serving
predictions from these models to meet any target latency and cost requirements
of applications remains a key challenge, despite recent work in building
inference-serving systems as well as algorithmic approaches that dynamically
adapt models based on inputs. In this paper, we introduce a form of dynamism,
modality selection, where we adaptively choose modalities from inference inputs
while maintaining the model quality. We introduce MOSEL, an automated inference
serving system for multi-modal ML models that carefully picks input modalities
per request based on user-defined performance and accuracy requirements. MOSEL
exploits modality configurations extensively, improving system throughput by
3.6$\times$ with an accuracy guarantee and shortening job completion times by
11$\times$.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Technical Report: Large Language Models can Strategically Deceive their Users when Put Under Pressure
Abstract: We demonstrate a situation in which Large Language Models, trained to be
helpful, harmless, and honest, can display misaligned behavior and
strategically deceive their users about this behavior without being instructed
to do so. Concretely, we deploy GPT-4 as an agent in a realistic, simulated
environment, where it assumes the role of an autonomous stock trading agent.
Within this environment, the model obtains an insider tip about a lucrative
stock trade and acts upon it despite knowing that insider trading is
disapproved of by company management. When reporting to its manager, the model
consistently hides the genuine reasons behind its trading decision. We perform
a brief investigation of how this behavior varies under changes to the setting,
such as removing model access to a reasoning scratchpad, attempting to prevent
the misaligned behavior by changing system instructions, changing the amount of
pressure the model is under, varying the perceived risk of getting caught, and
making other simple changes to the environment. To our knowledge, this is the
first demonstration of Large Language Models trained to be helpful, harmless,
and honest, strategically deceiving their users in a realistic situation
without direct instructions or training for deception.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)
Abstract: The advent of foundation models signals a new era in artificial intelligence.
The Segment Anything Model (SAM) is the first foundation model for image
segmentation. In this study, we evaluate SAM's ability to segment features from
eye images recorded in virtual reality setups. The increasing requirement for
annotated eye-image datasets presents a significant opportunity for SAM to
redefine the landscape of data annotation in gaze estimation. Our investigation
centers on SAM's zero-shot learning abilities and the effectiveness of prompts
like bounding boxes or point clicks. Our results are consistent with studies in
other domains, demonstrating that SAM's segmentation effectiveness can be
on-par with specialized models depending on the feature, with prompts improving
its performance, evidenced by an IoU of 93.34% for pupil segmentation in one
dataset. Foundation models like SAM could revolutionize gaze estimation by
enabling quick and easy image segmentation, reducing reliance on specialized
models and extensive manual annotation.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: GLIME: General, Stable and Local LIME Explanation
Abstract: As black-box machine learning models grow in complexity and find applications
in high-stakes scenarios, it is imperative to provide explanations for their
predictions. Although Local Interpretable Model-agnostic Explanations (LIME)
[22] is a widely adpoted method for understanding model behaviors, it is
unstable with respect to random seeds [35,24,3] and exhibits low local fidelity
(i.e., how well the explanation approximates the model's local behaviors)
[21,16]. Our study shows that this instability problem stems from small sample
weights, leading to the dominance of regularization and slow convergence.
Additionally, LIME's sampling neighborhood is non-local and biased towards the
reference, resulting in poor local fidelity and sensitivity to reference
choice. To tackle these challenges, we introduce GLIME, an enhanced framework
extending LIME and unifying several prior methods. Within the GLIME framework,
we derive an equivalent formulation of LIME that achieves significantly faster
convergence and improved stability. By employing a local and unbiased sampling
distribution, GLIME generates explanations with higher local fidelity compared
to LIME. GLIME explanations are independent of reference choice. Moreover,
GLIME offers users the flexibility to choose a sampling distribution based on
their specific scenarios.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques
Abstract: Content-based image retrieval (CBIR) systems have emerged as crucial tools in
the field of computer vision, allowing for image search based on visual content
rather than relying solely on metadata. This survey paper presents a
comprehensive overview of CBIR, emphasizing its role in object detection and
its potential to identify and retrieve visually similar images based on content
features. Challenges faced by CBIR systems, including the semantic gap and
scalability, are discussed, along with potential solutions. It elaborates on
the semantic gap, which arises from the disparity between low-level features
and high-level semantic concepts, and explores approaches to bridge this gap.
One notable solution is the integration of relevance feedback (RF), empowering
users to provide feedback on retrieved images and refine search results
iteratively. The survey encompasses long-term and short-term learning
approaches that leverage RF for enhanced CBIR accuracy and relevance. These
methods focus on weight optimization and the utilization of active learning
algorithms to select samples for training classifiers. Furthermore, the paper
investigates machine learning techniques and the utilization of deep learning
and convolutional neural networks to enhance CBIR performance. This survey
paper plays a significant role in advancing the understanding of CBIR and RF
techniques. It guides researchers and practitioners in comprehending existing
methodologies, challenges, and potential solutions while fostering knowledge
dissemination and identifying research gaps. By addressing future research
directions, it sets the stage for advancements in CBIR that will enhance
retrieval accuracy, usability, and effectiveness in various application
domains.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Integrating AI into CCTV Systems: A Comprehensive Evaluation of Smart Video Surveillance in Community Space
Abstract: This article presents an AI-enabled Smart Video Surveillance (SVS) designed
to enhance safety in community spaces such as educational and recreational
areas, and small businesses. The proposed system innovatively integrates with
existing CCTV and wired camera networks, simplifying its adoption across
various community cases to leverage recent AI advancements. Our SVS system,
focusing on privacy, uses metadata instead of pixel data for activity
recognition, aligning with ethical standards. It features cloud-based
infrastructure and a mobile app for real-time, privacy-conscious alerts in
communities.
  This article notably pioneers a comprehensive real-world evaluation of the
SVS system, covering AI-driven visual processing, statistical analysis,
database management, cloud communication, and user notifications. It's also the
first to assess an end-to-end anomaly detection system's performance, vital for
identifying potential public safety incidents.
  For our evaluation, we implemented the system in a community college, serving
as an ideal model to exemplify the proposed system's capabilities. Our findings
in this setting demonstrate the system's robustness, with throughput, latency,
and scalability effectively managing 16 CCTV cameras. The system maintained a
consistent 16.5 frames per second (FPS) over a 21-hour operation. The average
end-to-end latency for detecting behavioral anomalies and alerting users was
26.76 seconds.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Health Disparities through Generative AI Models: A Comparison Study Using A Domain Specific large language model
Abstract: Health disparities are differences in health outcomes and access to
healthcare between different groups, including racial and ethnic minorities,
low-income people, and rural residents. An artificial intelligence (AI) program
called large language models (LLMs) can understand and generate human language,
improving health communication and reducing health disparities. There are many
challenges in using LLMs in human-doctor interaction, including the need for
diverse and representative data, privacy concerns, and collaboration between
healthcare providers and technology experts. We introduce the comparative
investigation of domain-specific large language models such as SciBERT with a
multi-purpose LLMs BERT. We used cosine similarity to analyze text queries
about health disparities in exam rooms when factors such as race are used
alone. Using text queries, SciBERT fails when it doesn't differentiate between
queries text: "race" alone and "perpetuates health disparities." We believe
clinicians can use generative AI to create a draft response when communicating
asynchronously with patients. However, careful attention must be paid to ensure
they are developed and implemented ethically and equitably.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Ethical implications of ChatGPT in higher education: A scoping review
Abstract: This scoping review explores the ethical challenges of using ChatGPT in
education, focusing particularly on issues related to higher education. By
reviewing recent academic articles written in English, Chinese, and Japanese,
we aimed to provide a comprehensive overview of relevant research while
identifying gaps for future considerations. Drawing on Arksey and O'Malley's
(2005) five-stage scoping review framework, we identified research questions,
search terms, and conducted article search from four databases in the target
three languages. Each article was reviewed by at least two researchers
identifying the main ethical issues of utilizing AI in education, particularly
higher education. Our analysis of ethical issues followed the framework
developed by DeepMind (Weiginger et al., 2021) to identify six main areas of
ethical concern in Language Models. The majority of papers were concerned with
misinformation harms (n=25) and/or human-computer interaction related harms
(n=24). Given the rapid deployment of Generative Artificial Intelligence (GAI),
it is imperative for educators to conduct more empirical studies to develop
sound ethical policies for the use of GAI.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Path Analysis for Effective Fault Localization in Deep Neural Networks
Abstract: Deep learning has revolutionized various real-world applications, but the
quality of Deep Neural Networks (DNNs) remains a concern. DNNs are complex and
have millions of parameters, making it difficult to determine their
contributions to fulfilling a task. Moreover, the behavior of a DNN is highly
influenced by the data used during training, making it challenging to collect
enough data to exercise all potential DNN behavior under all possible
scenarios. This paper proposes NP SBFL method to locate faulty neural pathways
(NP) using spectrum-based fault localization (SBFL). Our method identifies
critical neurons using the layer-wise relevance propagation (LRP) technique and
determines which critical neurons are faulty. Moreover, we propose a
multi-stage gradient ascent (MGA), an extension of gradient ascent (GA), to
effectively activate a sequence of neurons one at a time while maintaining the
activation of previous neurons, so we are able to test the reported faulty
pathways. We evaluated the effectiveness of our method, i.e. NP-SBFL-MGA, on
two commonly used datasets, MNIST and CIFAR-10, two baselines DeepFault and
NP-SBFL-GA, and three suspicious neuron measures, Tarantula, Ochiai, and
Barinel. The empirical results showed that NP-SBFL-MGA is statistically more
effective than the baselines at identifying suspicious paths and synthesizing
adversarial inputs. Particularly, Tarantula on NP-SBFL-MGA had the highest
fault detection rate at 96.75%, surpassing DeepFault on Ochiai (89.90%) and
NP-SBFL-GA on Ochiai (60.61%). Our approach also yielded comparable results to
the baselines in synthesizing naturalness inputs, and we found a positive
correlation between the coverage of critical paths and the number of failed
tests in DNN fault localization.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: PhayaThaiBERT: Enhancing a Pretrained Thai Language Model with Unassimilated Loanwords
Abstract: While WangchanBERTa has become the de facto standard in transformer-based
Thai language modeling, it still has shortcomings in regard to the
understanding of foreign words, most notably English words, which are often
borrowed without orthographic assimilation into Thai in many contexts. We
identify the lack of foreign vocabulary in WangchanBERTa's tokenizer as the
main source of these shortcomings. We then expand WangchanBERTa's vocabulary
via vocabulary transfer from XLM-R's pretrained tokenizer and pretrain a new
model using the expanded tokenizer, starting from WangchanBERTa's checkpoint,
on a new dataset that is larger than the one used to train WangchanBERTa. Our
results show that our new pretrained model, PhayaThaiBERT, outperforms
WangchanBERTa in many downstream tasks and datasets.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Dense X Retrieval: What Retrieval Granularity Should We Use?
Abstract: Dense retrieval has become a prominent method to obtain relevant context or
world knowledge in open-domain NLP tasks. When we use a learned dense retriever
on a retrieval corpus at inference time, an often-overlooked design choice is
the retrieval unit in which the corpus is indexed, e.g. document, passage, or
sentence. We discover that the retrieval unit choice significantly impacts the
performance of both retrieval and downstream tasks. Distinct from the typical
approach of using passages or sentences, we introduce a novel retrieval unit,
proposition, for dense retrieval. Propositions are defined as atomic
expressions within text, each encapsulating a distinct factoid and presented in
a concise, self-contained natural language format. We conduct an empirical
comparison of different retrieval granularity. Our results reveal that
proposition-based retrieval significantly outperforms traditional passage or
sentence-based methods in dense retrieval. Moreover, retrieval by proposition
also enhances the performance of downstream QA tasks, since the retrieved texts
are more condensed with question-relevant information, reducing the need for
lengthy input tokens and minimizing the inclusion of extraneous, irrelevant
information.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Mental Health Diagnosis in the Digital Age: Harnessing Sentiment Analysis on Social Media Platforms upon Ultra-Sparse Feature Content
Abstract: Amid growing global mental health concerns, particularly among vulnerable
groups, natural language processing offers a tremendous potential for early
detection and intervention of people's mental disorders via analyzing their
postings and discussions on social media platforms. However, ultra-sparse
training data, often due to vast vocabularies and low-frequency words, hinders
the analysis accuracy. Multi-labeling and Co-occurrences of symptoms may also
blur the boundaries in distinguishing similar/co-related disorders. To address
these issues, we propose a novel semantic feature preprocessing technique with
a three-folded structure: 1) mitigating the feature sparsity with a weak
classifier, 2) adaptive feature dimension with modulus loops, and 3)
deep-mining and extending features among the contexts. With enhanced semantic
features, we train a machine learning model to predict and classify mental
disorders. We utilize the Reddit Mental Health Dataset 2022 to examine
conditions such as Anxiety, Borderline Personality Disorder (BPD), and
Bipolar-Disorder (BD) and present solutions to the data sparsity challenge,
highlighted by 99.81% non-zero elements. After applying our preprocessing
technique, the feature sparsity decreases to 85.4%. Overall, our methods, when
compared to seven benchmark models, demonstrate significant performance
improvements: 8.0% in accuracy, 0.069 in precision, 0.093 in recall, 0.102 in
F1 score, and 0.059 in AUC. This research provides foundational insights for
mental health prediction and monitoring, providing innovative solutions to
navigate challenges associated with ultra-sparse data feature and intricate
multi-label classification in the domain of mental health analysis.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Beyond English: Evaluating LLMs for Arabic Grammatical Error Correction
Abstract: Large language models (LLMs) finetuned to follow human instruction have
recently exhibited significant capabilities in various English NLP tasks.
However, their performance in grammatical error correction (GEC), especially on
languages other than English, remains significantly unexplored. In this work,
we evaluate the abilities of instruction finetuned LLMs in Arabic GEC, a
complex task due to Arabic's rich morphology. Our findings suggest that various
prompting methods, coupled with (in-context) few-shot learning, demonstrate
considerable effectiveness, with GPT-4 achieving up to $65.49$ F$_{1}$ score
under expert prompting (approximately $5$ points higher than our established
baseline). Despite these positive results, we find that instruction finetuned
models, regardless of their size, are still outperformed by fully finetuned
ones, even if they are significantly smaller in size. This disparity highlights
substantial room for improvements for LLMs. Inspired by methods used in
low-resource machine translation, we also develop a method exploiting synthetic
data that significantly outperforms previous models on two standard Arabic
benchmarks. Our best model achieves a new SOTA on Arabic GEC, with $73.29$ and
$73.26$ F$_{1}$ on the 2014 and 2015 QALB datasets, respectively, compared to
peer-reviewed published baselines.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Evaluating The Accuracy of Classification Algorithms for Detecting Heart Disease Risk
Abstract: The healthcare industry generates enormous amounts of complex clinical data
that make the prediction of disease detection a complicated process. In medical
informatics, making effective and efficient decisions is very important. Data
Mining (DM) techniques are mainly used to identify and extract hidden patterns
and interesting knowledge to diagnose and predict diseases in medical datasets.
Nowadays, heart disease is considered one of the most important problems in the
healthcare field. Therefore, early diagnosis leads to a reduction in deaths. DM
techniques have proven highly effective for predicting and diagnosing heart
diseases. This work utilizes the classification algorithms with a medical
dataset of heart disease; namely, J48, Random Forest, and Na\"ive Bayes to
discover the accuracy of their performance. We also examine the impact of the
feature selection method. A comparative and analysis study was performed to
determine the best technique using Waikato Environment for Knowledge Analysis
(Weka) software, version 3.8.6. The performance of the utilized algorithms was
evaluated using standard metrics such as accuracy, sensitivity and specificity.
The importance of using classification techniques for heart disease diagnosis
has been highlighted. We also reduced the number of attributes in the dataset,
which showed a significant improvement in prediction accuracy. The results
indicate that the best algorithm for predicting heart disease was Random Forest
with an accuracy of 99.24%.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Outcome-supervised Verifiers for Planning in Mathematical Reasoning
Abstract: Large language models (LLMs) often struggle with maintaining accuracy across
a sequence of intermediate reasoning steps in mathematical reasoning, leading
to error propagation that undermines the final result. The current methodology
to mitigate this issue primarily involves using a verifier model to assess the
correctness of generated solution candidates, focusing either on the overall
reasoning path or on an incomplete reasoning path. By rethinking this approach,
we argue that assessing potentials of incomplete reasoning paths could be more
advantageous as it guides towards correct final answers, transforming the task
into a \textit{planning} problem. Our proposed verifier, the
Outcome-supervision Value Model (OVM), employs outcome supervision for
training, offering an efficient and intuitive method for \textit{planning} by
prioritizing steps that lead to accurate conclusions over mere per-step
correctness. Furthermore, the OVM eschews the need for labor-intensive
annotations on step-level correctness, enhancing its scalability. Our
experiments on two multi-step mathematical reasoning datasets, GSM8K and Game
of 24, demonstrate the superior performance of the OVM model. Notably, in
GSM8K, our \textbf{OVM-7B model achieves state-of-the-art results among LLMs up
to 13B parameters}; especially it does not utilize GPT-4 or code execution.
These findings offer a novel perspective on the role of outcome supervision in
training verifiers for multi-step reasoning tasks and provide theoretical
justification for its advantage in value estimation for planning.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Self Attention with Temporal Prior: Can We Learn More from Arrow of Time?
Abstract: Many of diverse phenomena in nature often inherently encode both short and
long term temporal dependencies, short term dependencies especially resulting
from the direction of flow of time. In this respect, we discovered experimental
evidences suggesting that {\it interrelations} of these events are higher for
closer time stamps. However, to be able for attention based models to learn
these regularities in short term dependencies, it requires large amounts of
data which are often infeasible. This is due to the reason that, while they are
good at learning piece wised temporal dependencies, attention based models lack
structures that encode biases in time series. As a resolution, we propose a
simple and efficient method that enables attention layers to better encode
short term temporal bias of these data sets by applying learnable, adaptive
kernels directly to the attention matrices. For the experiments, we chose
various prediction tasks using Electronic Health Records (EHR) data sets since
they are great examples that have underlying long and short term temporal
dependencies. The results of our experiments show exceptional classification
results compared to best performing models on most of the task and data sets.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM
Abstract: Existing open-source helpfulness preference datasets do not specify what
makes some responses more helpful and others less so. Models trained on these
datasets can incidentally learn to model dataset artifacts (e.g. preferring
longer but unhelpful responses only due to their length). To alleviate this
problem, we collect HelpSteer, a multi-attribute helpfulness dataset annotated
for the various aspects that make responses helpful. Specifically, our
37k-sample dataset has annotations for correctness, coherence, complexity, and
verbosity in addition to overall helpfulness of responses. Training Llama 2 70B
using the HelpSteer dataset with SteerLM technique produces a model that scores
7.54 on MT Bench, which is currently the highest score for open models that do
not require training data from more powerful models (e.g. GPT4). We release
this dataset with CC-BY-4.0 license at
https://huggingface.co/datasets/nvidia/HelpSteer

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles
Abstract: Large language model (LLM) prompting is a promising new approach for users to
create and customize their own chatbots. However, current methods for steering
a chatbot's outputs, such as prompt engineering and fine-tuning, do not support
users in converting their natural feedback on the model's outputs to changes in
the prompt or model. In this work, we explore how to enable users to
interactively refine model outputs through their feedback, by helping them
convert their feedback into a set of principles (i.e. a constitution) that
dictate the model's behavior. From a formative study, we (1) found that users
needed support converting their feedback into principles for the chatbot and
(2) classified the different principle types desired by users. Inspired by
these findings, we developed ConstitutionMaker, an interactive tool for
converting user feedback into principles, to steer LLM-based chatbots. With
ConstitutionMaker, users can provide either positive or negative feedback in
natural language, select auto-generated feedback, or rewrite the chatbot's
response; each mode of feedback automatically generates a principle that is
inserted into the chatbot's prompt. In a user study with 14 participants, we
compare ConstitutionMaker to an ablated version, where users write their own
principles. With ConstitutionMaker, participants felt that their principles
could better guide the chatbot, that they could more easily convert their
feedback into principles, and that they could write principles more
efficiently, with less mental demand. ConstitutionMaker helped users identify
ways to improve the chatbot, formulate their intuitive responses to the model
into feedback, and convert this feedback into specific and clear principles.
Together, these findings inform future tools that support the interactive
critiquing of LLM outputs.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4
Abstract: In recent years, groundbreaking advancements in natural language processing
have culminated in the emergence of powerful large language models (LLMs),
which have showcased remarkable capabilities across a vast array of domains,
including the understanding, generation, and translation of natural language,
and even tasks that extend beyond language processing. In this report, we delve
into the performance of LLMs within the context of scientific discovery,
focusing on GPT-4, the state-of-the-art language model. Our investigation spans
a diverse range of scientific areas encompassing drug discovery, biology,
computational chemistry (density functional theory (DFT) and molecular dynamics
(MD)), materials design, and partial differential equations (PDE). Evaluating
GPT-4 on scientific tasks is crucial for uncovering its potential across
various research domains, validating its domain-specific expertise,
accelerating scientific progress, optimizing resource allocation, guiding
future model development, and fostering interdisciplinary research. Our
exploration methodology primarily consists of expert-driven case assessments,
which offer qualitative insights into the model's comprehension of intricate
scientific concepts and relationships, and occasionally benchmark testing,
which quantitatively evaluates the model's capacity to solve well-defined
domain-specific problems. Our preliminary exploration indicates that GPT-4
exhibits promising potential for a variety of scientific applications,
demonstrating its aptitude for handling complex problem-solving and knowledge
integration tasks. Broadly speaking, we evaluate GPT-4's knowledge base,
scientific understanding, scientific numerical calculation abilities, and
various scientific prediction capabilities.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Clustering Pseudo Language Family in Multilingual Translation Models with Fisher Information Matrix
Abstract: In multilingual translation research, the comprehension and utilization of
language families are of paramount importance. Nevertheless, clustering
languages based solely on their ancestral families can yield suboptimal results
due to variations in the datasets employed during the model's training phase.
To mitigate this challenge, we introduce an innovative method that leverages
the fisher information matrix (FIM) to cluster language families, anchored on
the multilingual translation model's characteristics. We hypothesize that
language pairs with similar effects on model parameters exhibit a considerable
degree of linguistic congruence and should thus be grouped cohesively. This
concept has led us to define pseudo language families. We provide an in-depth
discussion regarding the inception and application of these pseudo language
families. Empirical evaluations reveal that employing these pseudo language
families enhances performance over conventional language families in adapting a
multilingual translation model to unfamiliar language pairs. The proposed
methodology may also be extended to scenarios requiring language similarity
measurements. The source code and associated scripts can be accessed at
https://github.com/ecoli-hit/PseudoFamily.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Learning to Filter Context for Retrieval-Augmented Generation
Abstract: On-the-fly retrieval of relevant knowledge has proven an essential element of
reliable systems for tasks such as open-domain question answering and fact
verification. However, because retrieval systems are not perfect, generation
models are required to generate outputs given partially or entirely irrelevant
passages. This can cause over- or under-reliance on context, and result in
problems in the generated output such as hallucinations. To alleviate these
problems, we propose FILCO, a method that improves the quality of the context
provided to the generator by (1) identifying useful context based on lexical
and information-theoretic approaches, and (2) training context filtering models
that can filter retrieved contexts at test time. We experiment on six
knowledge-intensive tasks with FLAN-T5 and LLaMa2, and demonstrate that our
method outperforms existing approaches on extractive question answering (QA),
complex multi-hop and long-form QA, fact verification, and dialog generation
tasks. FILCO effectively improves the quality of context, whether or not it
supports the canonical output.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Quantifying the redundancy between prosody and text
Abstract: Prosody -- the suprasegmental component of speech, including pitch, loudness,
and tempo -- carries critical aspects of meaning. However, the relationship
between the information conveyed by prosody vs. by the words themselves remains
poorly understood. We use large language models (LLMs) to estimate how much
information is redundant between prosody and the words themselves. Using a
large spoken corpus of English audiobooks, we extract prosodic features aligned
to individual words and test how well they can be predicted from LLM
embeddings, compared to non-contextual word embeddings. We find a high degree
of redundancy between the information carried by the words and prosodic
information across several prosodic features, including intensity, duration,
pauses, and pitch contours. Furthermore, a word's prosodic information is
redundant with both the word itself and the context preceding as well as
following it. Still, we observe that prosodic features can not be fully
predicted from text, suggesting that prosody carries information above and
beyond the words. Along with this paper, we release a general-purpose data
processing pipeline for quantifying the relationship between linguistic
information and extra-linguistic features.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
Abstract: Model pre-training is essential in human-centric perception. In this paper,
we first introduce masked image modeling (MIM) as a pre-training approach for
this task. Upon revisiting the MIM training strategy, we reveal that human
structure priors offer significant potential. Motivated by this insight, we
further incorporate an intuitive human structure prior - human parts - into
pre-training. Specifically, we employ this prior to guide the mask sampling
process. Image patches, corresponding to human part regions, have high priority
to be masked out. This encourages the model to concentrate more on body
structure information during pre-training, yielding substantial benefits across
a range of human-centric perception tasks. To further capture human
characteristics, we propose a structure-invariant alignment loss that enforces
different masked views, guided by the human part prior, to be closely aligned
for the same image. We term the entire method as HAP. HAP simply uses a plain
ViT as the encoder yet establishes new state-of-the-art performance on 11
human-centric benchmarks, and on-par result on one dataset. For example, HAP
achieves 78.1% mAP on MSMT17 for person re-identification, 86.54% mA on PA-100K
for pedestrian attribute recognition, 78.2% AP on MS COCO for 2D pose
estimation, and 56.0 PA-MPJPE on 3DPW for 3D pose and shape estimation.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values
Abstract: Massive transformer-based models face several challenges, including slow and
computationally intensive pre-training and over-parametrization. This paper
addresses these challenges by proposing a versatile method called GQKVA, which
generalizes query, key, and value grouping techniques. GQKVA is designed to
speed up transformer pre-training while reducing the model size. Our
experiments with various GQKVA variants highlight a clear trade-off between
performance and model size, allowing for customized choices based on resource
and time limitations. Our findings also indicate that the conventional
multi-head attention approach is not always the best choice, as there are
lighter and faster alternatives available. We tested our method on ViT, which
achieved an approximate 0.3% increase in accuracy while reducing the model size
by about 4% in the task of image classification. Additionally, our most
aggressive model reduction experiment resulted in a reduction of approximately
15% in model size, with only around a 1% drop in accuracy.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: TCM-GPT: Efficient Pre-training of Large Language Models for Domain Adaptation in Traditional Chinese Medicine
Abstract: Pre-training and fine-tuning have emerged as a promising paradigm across
various natural language processing (NLP) tasks. The effectiveness of
pretrained large language models (LLM) has witnessed further enhancement,
holding potential for applications in the field of medicine, particularly in
the context of Traditional Chinese Medicine (TCM). However, the application of
these general models to specific domains often yields suboptimal results,
primarily due to challenges like lack of domain knowledge, unique objectives,
and computational efficiency. Furthermore, their effectiveness in specialized
domains, such as Traditional Chinese Medicine, requires comprehensive
evaluation. To address the above issues, we propose a novel domain specific
TCMDA (TCM Domain Adaptation) approach, efficient pre-training with
domain-specific corpus. Specifically, we first construct a large TCM-specific
corpus, TCM-Corpus-1B, by identifying domain keywords and retreving from
general corpus. Then, our TCMDA leverages the LoRA which freezes the pretrained
model's weights and uses rank decomposition matrices to efficiently train
specific dense layers for pre-training and fine-tuning, efficiently aligning
the model with TCM-related tasks, namely TCM-GPT-7B. We further conducted
extensive experiments on two TCM tasks, including TCM examination and TCM
diagnosis. TCM-GPT-7B archived the best performance across both datasets,
outperforming other models by relative increments of 17% and 12% in accuracy,
respectively. To the best of our knowledge, our study represents the pioneering
validation of domain adaptation of a large language model with 7 billion
parameters in TCM domain. We will release both TCMCorpus-1B and TCM-GPT-7B
model once accepted to facilitate interdisciplinary development in TCM and NLP,
serving as the foundation for further study.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Prediction of rare events in the operation of household equipment using co-evolving time series
Abstract: In this study, we propose an approach for predicting rare events by
exploiting time series in coevolution. Our approach involves a weighted
autologistic regression model, where we leverage the temporal behavior of the
data to enhance predictive capabilities. By addressing the issue of imbalanced
datasets, we establish constraints leading to weight estimation and to improved
performance. Evaluation on synthetic and real-world datasets confirms that our
approach outperform state-of-the-art of predicting home equipment failure
methods.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking
Abstract: Standard Full-Data classifiers in NLP demand thousands of labeled examples,
which is impractical in data-limited domains. Few-shot methods offer an
alternative, utilizing contrastive learning techniques that can be effective
with as little as 20 examples per class. Similarly, Large Language Models
(LLMs) like GPT-4 can perform effectively with just 1-5 examples per class.
However, the performance-cost trade-offs of these methods remain underexplored,
a critical concern for budget-limited organizations. Our work addresses this
gap by studying the aforementioned approaches over the Banking77 financial
intent detection dataset, including the evaluation of cutting-edge LLMs by
OpenAI, Cohere, and Anthropic in a comprehensive set of few-shot scenarios. We
complete the picture with two additional methods: first, a cost-effective
querying method for LLMs based on retrieval-augmented generation (RAG), able to
reduce operational costs multiple times compared to classic few-shot
approaches, and second, a data augmentation method using GPT-4, able to improve
performance in data-limited scenarios. Finally, to inspire future research, we
provide a human expert's curated subset of Banking77, along with extensive
error analysis.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Assessing the Usability of GutGPT: A Simulation Study of an AI Clinical Decision Support System for Gastrointestinal Bleeding Risk
Abstract: Applications of large language models (LLMs) like ChatGPT have potential to
enhance clinical decision support through conversational interfaces. However,
challenges of human-algorithmic interaction and clinician trust are poorly
understood. GutGPT, a LLM for gastrointestinal (GI) bleeding risk prediction
and management guidance, was deployed in clinical simulation scenarios
alongside the electronic health record (EHR) with emergency medicine
physicians, internal medicine physicians, and medical students to evaluate its
effect on physician acceptance and trust in AI clinical decision support
systems (AI-CDSS). GutGPT provides risk predictions from a validated machine
learning model and evidence-based answers by querying extracted clinical
guidelines. Participants were randomized to GutGPT and an interactive
dashboard, or the interactive dashboard and a search engine. Surveys and
educational assessments taken before and after measured technology acceptance
and content mastery. Preliminary results showed mixed effects on acceptance
after using GutGPT compared to the dashboard or search engine but appeared to
improve content mastery based on simulation performance. Overall, this study
demonstrates LLMs like GutGPT could enhance effective AI-CDSS if implemented
optimally and paired with interactive interfaces.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Can LLMs Follow Simple Rules?
Abstract: As Large Language Models (LLMs) are deployed with increasing real-world
responsibilities, it is important to be able to specify and constrain the
behavior of these systems in a reliable manner. Model developers may wish to
set explicit rules for the model, such as "do not generate abusive content",
but these may be circumvented by jailbreaking techniques. Evaluating how well
LLMs follow developer-provided rules in the face of adversarial inputs
typically requires manual review, which slows down monitoring and methods
development. To address this issue, we propose Rule-following Language
Evaluation Scenarios (RuLES), a programmatic framework for measuring
rule-following ability in LLMs. RuLES consists of 15 simple text scenarios in
which the model is instructed to obey a set of rules in natural language while
interacting with the human user. Each scenario has a concise evaluation program
to determine whether the model has broken any rules in a conversation. Through
manual exploration of model behavior in our scenarios, we identify 6 categories
of attack strategies and collect two suites of test cases: one consisting of
unique conversations from manual testing and one that systematically implements
strategies from the 6 categories. Across various popular proprietary and open
models such as GPT-4 and Llama 2, we find that all models are susceptible to a
wide variety of adversarial hand-crafted user inputs, though GPT-4 is the
best-performing model. Additionally, we evaluate open models under
gradient-based attacks and find significant vulnerabilities. We propose RuLES
as a challenging new setting for research into exploring and defending against
both manual and automatic attacks on LLMs.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Deeper Understanding of Black-box Predictions via Generalized Influence Functions
Abstract: Influence functions (IFs) elucidate how learning data affects model behavior.
However, growing non-convexity and the number of parameters in modern
large-scale models lead to imprecise influence approximation and instability in
computations. We highly suspect that the first-order approximation in large
models causes such fragility, as IFs change all parameters including possibly
nuisance parameters that are irrelevant to the examined data. Thus, we attempt
to selectively analyze parameters associated with the data. However, simply
computing influence from the chosen parameters can be misleading, as it fails
to nullify the subliminal impact of unselected parameters. Our approach
introduces generalized IFs, precisely estimating target parameters' influence
while considering fixed parameters' effects. Unlike the classic IFs, we newly
adopt a method to identify pertinent target parameters closely associated with
the analyzed data. Furthermore, we tackle computational instability with a
robust inverse-Hessian-vector product approximation. Remarkably, the proposed
approximation algorithm guarantees convergence regardless of the network
configurations. We evaluated our approach on ResNet-18 and VGG-11 for class
removal and backdoor model recovery. Modifying just 10\% of the network yields
results comparable to the network retrained from scratch. Aligned with our
first guess, we also confirm that modifying an excessive number of parameters
results in a decline in network utility. We believe our proposal can become a
versatile tool for model analysis across various AI domains, appealing to both
specialists and general readers. Codes are available at
https://github.com/hslyu/GIF.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: gcDLSeg: Integrating Graph-cut into Deep Learning for Binary Semantic Segmentation
Abstract: Binary semantic segmentation in computer vision is a fundamental problem. As
a model-based segmentation method, the graph-cut approach was one of the most
successful binary segmentation methods thanks to its global optimality
guarantee of the solutions and its practical polynomial-time complexity.
Recently, many deep learning (DL) based methods have been developed for this
task and yielded remarkable performance, resulting in a paradigm shift in this
field. To combine the strengths of both approaches, we propose in this study to
integrate the graph-cut approach into a deep learning network for end-to-end
learning. Unfortunately, backward propagation through the graph-cut module in
the DL network is challenging due to the combinatorial nature of the graph-cut
algorithm. To tackle this challenge, we propose a novel residual graph-cut loss
and a quasi-residual connection, enabling the backward propagation of the
gradients of the residual graph-cut loss for effective feature learning guided
by the graph-cut segmentation model. In the inference phase, globally optimal
segmentation is achieved with respect to the graph-cut energy defined on the
optimized image features learned from DL networks. Experiments on the public
AZH chronic wound data set and the pancreas cancer data set from the medical
segmentation decathlon (MSD) demonstrated promising segmentation accuracy, and
improved robustness against adversarial attacks.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Improving Fairness using Vision-Language Driven Image Augmentation
Abstract: Fairness is crucial when training a deep-learning discriminative model,
especially in the facial domain. Models tend to correlate specific
characteristics (such as age and skin color) with unrelated attributes
(downstream tasks), resulting in biases which do not correspond to reality. It
is common knowledge that these correlations are present in the data and are
then transferred to the models during training. This paper proposes a method to
mitigate these correlations to improve fairness. To do so, we learn
interpretable and meaningful paths lying in the semantic space of a pre-trained
diffusion model (DiffAE) -- such paths being supervised by contrastive text
dipoles. That is, we learn to edit protected characteristics (age and skin
color). These paths are then applied to augment images to improve the fairness
of a given dataset. We test the proposed method on CelebA-HQ and UTKFace on
several downstream tasks with age and skin color as protected characteristics.
As a proxy for fairness, we compute the difference in accuracy with respect to
the protected characteristics. Quantitative results show how the augmented
images help the model improve the overall accuracy, the aforementioned metric,
and the disparity of equal opportunity. Code is available at:
https://github.com/Moreno98/Vision-Language-Bias-Control.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Personalized Speech-driven Expressive 3D Facial Animation Synthesis with Style Control
Abstract: Different people have different facial expressions while speaking
emotionally. A realistic facial animation system should consider such
identity-specific speaking styles and facial idiosyncrasies to achieve
high-degree of naturalness and plausibility. Existing approaches to
personalized speech-driven 3D facial animation either use one-hot identity
labels or rely-on person specific models which limit their scalability. We
present a personalized speech-driven expressive 3D facial animation synthesis
framework that models identity specific facial motion as latent representations
(called as styles), and synthesizes novel animations given a speech input with
the target style for various emotion categories. Our framework is trained in an
end-to-end fashion and has a non-autoregressive encoder-decoder architecture
with three main components: expression encoder, speech encoder and expression
decoder. Since, expressive facial motion includes both identity-specific style
and speech-related content information; expression encoder first disentangles
facial motion sequences into style and content representations, respectively.
Then, both of the speech encoder and the expression decoders input the
extracted style information to update transformer layer weights during training
phase. Our speech encoder also extracts speech phoneme label and duration
information to achieve better synchrony within the non-autoregressive synthesis
mechanism more effectively. Through detailed experiments, we demonstrate that
our approach produces temporally coherent facial expressions from input speech
while preserving the speaking styles of the target identities.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Human-centred explanation of rule-based decision-making systems in the legal domain
Abstract: We propose a human-centred explanation method for rule-based automated
decision-making systems in the legal domain. Firstly, we establish a conceptual
framework for developing explanation methods, representing its key internal
components (content, communication and adaptation) and external dependencies
(decision-making system, human recipient and domain). Secondly, we propose an
explanation method that uses a graph database to enable question-driven
explanations and multimedia display. This way, we can tailor the explanation to
the user. Finally, we show how our conceptual framework is applicable to a
real-world scenario at the Dutch Tax and Customs Administration and implement
our explanation method for this scenario.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving
Abstract: The ability to accurately predict the trajectory of surrounding vehicles is a
critical hurdle to overcome on the journey to fully autonomous vehicles. To
address this challenge, we pioneer a novel behavior-aware trajectory prediction
model (BAT) that incorporates insights and findings from traffic psychology,
human behavior, and decision-making. Our model consists of behavior-aware,
interaction-aware, priority-aware, and position-aware modules that perceive and
understand the underlying interactions and account for uncertainty and
variability in prediction, enabling higher-level learning and flexibility
without rigid categorization of driving behavior. Importantly, this approach
eliminates the need for manual labeling in the training process and addresses
the challenges of non-continuous behavior labeling and the selection of
appropriate time windows. We evaluate BAT's performance across the Next
Generation Simulation (NGSIM), Highway Drone (HighD), Roundabout Drone (RounD),
and Macao Connected Autonomous Driving (MoCAD) datasets, showcasing its
superiority over prevailing state-of-the-art (SOTA) benchmarks in terms of
prediction accuracy and efficiency. Remarkably, even when trained on reduced
portions of the training data (25%), our model outperforms most of the
baselines, demonstrating its robustness and efficiency in predicting vehicle
trajectories, and the potential to reduce the amount of data required to train
autonomous vehicles, especially in corner cases. In conclusion, the
behavior-aware model represents a significant advancement in the development of
autonomous vehicles capable of predicting trajectories with the same level of
proficiency as human drivers. The project page is available at
https://github.com/Petrichor625/BATraj-Behavior-aware-Model.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Do large language models solve verbal analogies like children do?
Abstract: Analogy-making lies at the heart of human cognition. Adults solve analogies
such as \textit{Horse belongs to stable like chicken belongs to ...?} by
mapping relations (\textit{kept in}) and answering \textit{chicken coop}. In
contrast, children often use association, e.g., answering \textit{egg}. This
paper investigates whether large language models (LLMs) solve verbal analogies
in A:B::C:? form using associations, similar to what children do. We use verbal
analogies extracted from an online adaptive learning environment, where 14,002
7-12 year-olds from the Netherlands solved 622 analogies in Dutch. The six
tested Dutch monolingual and multilingual LLMs performed around the same level
as children, with MGPT performing worst, around the 7-year-old level, and XLM-V
and GPT-3 the best, slightly above the 11-year-old level. However, when we
control for associative processes this picture changes and each model's
performance level drops 1-2 years. Further experiments demonstrate that
associative processes often underlie correctly solved analogies. We conclude
that the LLMs we tested indeed tend to solve verbal analogies by association
with C like children do.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Knowledge Corpus Error in Question Answering
Abstract: Recent works in open-domain question answering (QA) have explored generating
context passages from large language models (LLMs), replacing the traditional
retrieval step in the QA pipeline. However, it is not well understood why
generated passages can be more effective than retrieved ones. This study
revisits the conventional formulation of QA and introduces the concept of
knowledge corpus error. This error arises when the knowledge corpus used for
retrieval is only a subset of the entire string space, potentially excluding
more helpful passages that exist outside the corpus. LLMs may mitigate this
shortcoming by generating passages in a larger space. We come up with an
experiment of paraphrasing human-annotated gold context using LLMs to observe
knowledge corpus error empirically. Our results across three QA benchmarks
reveal an increased performance (10% - 13%) when using paraphrased passage,
indicating a signal for the existence of knowledge corpus error. Our code is
available at https://github.com/xfactlab/emnlp2023-knowledge-corpus-error

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Using Large Language Models for Hyperparameter Optimization
Abstract: This paper studies using foundational large language models (LLMs) to make
decisions during hyperparameter optimization (HPO). Empirical evaluations
demonstrate that in settings with constrained search budgets, LLMs can perform
comparably or better than traditional HPO methods like random search and
Bayesian optimization on standard benchmarks. Furthermore, we propose to treat
the code specifying our model as a hyperparameter, which the LLM outputs, going
beyond the capabilities of existing HPO approaches. Our findings suggest that
LLMs are a promising tool for improving efficiency in the traditional
decision-making problem of hyperparameter optimization.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Inherent limitations of LLMs regarding spatial information
Abstract: Despite the significant advancements in natural language processing
capabilities demonstrated by large language models such as ChatGPT, their
proficiency in comprehending and processing spatial information, especially
within the domains of 2D and 3D route planning, remains notably underdeveloped.
This paper investigates the inherent limitations of ChatGPT and similar models
in spatial reasoning and navigation-related tasks, an area critical for
applications ranging from autonomous vehicle guidance to assistive technologies
for the visually impaired. In this paper, we introduce a novel evaluation
framework complemented by a baseline dataset, meticulously crafted for this
study. This dataset is structured around three key tasks: plotting spatial
points, planning routes in two-dimensional (2D) spaces, and devising pathways
in three-dimensional (3D) environments. We specifically developed this dataset
to assess the spatial reasoning abilities of ChatGPT. Our evaluation reveals
key insights into the model's capabilities and limitations in spatial
understanding.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models
Abstract: Safety alignment of Large Language Models (LLMs) can be compromised with
manual jailbreak attacks and (automatic) adversarial attacks. Recent studies
suggest that defending against these attacks is possible: adversarial attacks
generate unlimited but unreadable gibberish prompts, detectable by
perplexity-based filters; manual jailbreak attacks craft readable prompts, but
their limited number due to the necessity of human creativity allows for easy
blocking. In this paper, we show that these solutions may be too optimistic. We
introduce AutoDAN, an interpretable, gradient-based adversarial attack that
merges the strengths of both attack types. Guided by the dual goals of
jailbreak and readability, AutoDAN optimizes and generates tokens one by one
from left to right, resulting in readable prompts that bypass perplexity
filters while maintaining high attack success rates. Notably, these prompts,
generated from scratch using gradients, are interpretable and diverse, with
emerging strategies commonly seen in manual jailbreak attacks. They also
generalize to unforeseen harmful behaviors and transfer to black-box LLMs
better than their unreadable counterparts when using limited training data or a
single proxy model. Furthermore, we show the versatility of AutoDAN by
automatically leaking system prompts using a customized objective. Our work
offers a new way to red-team LLMs and understand jailbreak mechanisms via
interpretability.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Evaluation of large language models using an Indian language LGBTI+ lexicon
Abstract: Large language models (LLMs) are typically evaluated on the basis of
task-based benchmarks such as MMLU. Such benchmarks do not examine responsible
behaviour of LLMs in specific contexts. This is particularly true in the LGBTI+
context where social stereotypes may result in variation in LGBTI+ terminology.
Therefore, domain-specific lexicons or dictionaries may be useful as a
representative list of words against which the LLM's behaviour needs to be
evaluated. This paper presents a methodology for evaluation of LLMs using an
LGBTI+ lexicon in Indian languages. The methodology consists of four steps:
formulating NLP tasks relevant to the expected behaviour, creating prompts that
test LLMs, using the LLMs to obtain the output and, finally, manually
evaluating the results. Our qualitative analysis shows that the three LLMs we
experiment on are unable to detect underlying hateful content. Similarly, we
observe limitations in using machine translation as means to evaluate natural
language understanding in languages other than English. The methodology
presented in this paper can be useful for LGBTI+ lexicons in other languages as
well as other domain-specific lexicons. The work done in this paper opens
avenues for responsible behaviour of LLMs, as demonstrated in the context of
prevalent social perception of the LGBTI+ community.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts
Abstract: Zero-shot Visual Question Answering (VQA) is a prominent vision-language task
that examines both the visual and textual understanding capability of systems
in the absence of training data. Recently, by converting the images into
captions, information across multi-modalities is bridged and Large Language
Models (LLMs) can apply their strong zero-shot generalization capability to
unseen questions. To design ideal prompts for solving VQA via LLMs, several
studies have explored different strategies to select or generate
question-answer pairs as the exemplar prompts, which guide LLMs to answer the
current questions effectively. However, they totally ignore the role of
question prompts. The original questions in VQA tasks usually encounter
ellipses and ambiguity which require intermediate reasoning. To this end, we
present Reasoning Question Prompts for VQA tasks, which can further activate
the potential of LLMs in zero-shot scenarios. Specifically, for each question,
we first generate self-contained questions as reasoning question prompts via an
unsupervised question edition module considering sentence fluency, semantic
integrity and syntactic invariance. Each reasoning question prompt clearly
indicates the intent of the original question. This results in a set of
candidate answers. Then, the candidate answers associated with their confidence
scores acting as answer heuristics are fed into LLMs and produce the final
answer. We evaluate reasoning question prompts on three VQA challenges,
experimental results demonstrate that they can significantly improve the
results of LLMs on zero-shot setting and outperform existing state-of-the-art
zero-shot methods on three out of four data sets. Our source code is publicly
released at \url{https://github.com/ECNU-DASE-NLP/RQP}.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Exploring Automatic Text Simplification of German Narrative Documents
Abstract: In this paper, we apply transformer-based Natural Language Generation (NLG)
techniques to the problem of text simplification. Currently, there are only a
few German datasets available for text simplification, even fewer with larger
and aligned documents, and not a single one with narrative texts. In this
paper, we explore to which degree modern NLG techniques can be applied to
German narrative text simplifications. We use Longformer attention and a
pre-trained mBART model. Our findings indicate that the existing approaches for
German are not able to solve the task properly. We conclude on a few directions
for future research to address this problem.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
Abstract: Clinical natural language processing requires methods that can address
domain-specific challenges, such as complex medical terminology and clinical
contexts. Recently, large language models (LLMs) have shown promise in this
domain. Yet, their direct deployment can lead to privacy issues and are
constrained by resources. To address this challenge, we delve into synthetic
clinical text generation using LLMs for clinical NLP tasks. We propose an
innovative, resource-efficient approach, ClinGen, which infuses knowledge into
the process. Our model involves clinical knowledge extraction and
context-informed LLM prompting. Both clinical topics and writing styles are
drawn from external domain-specific knowledge graphs and LLMs to guide data
generation. Our extensive empirical study across 7 clinical NLP tasks and 16
datasets reveals that ClinGen consistently enhances performance across various
tasks, effectively aligning the distribution of real datasets and significantly
enriching the diversity of generated training instances. We will publish our
code and all the generated data in \url{https://github.com/ritaranx/ClinGen}.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: MixtureGrowth: Growing Neural Networks by Recombining Learned Parameters
Abstract: Most deep neural networks are trained under fixed network architectures and
require retraining when the architecture changes. If expanding the network's
size is needed, it is necessary to retrain from scratch, which is expensive. To
avoid this, one can grow from a small network by adding random weights over
time to gradually achieve the target network size. However, this naive approach
falls short in practice as it brings too much noise to the growing process.
Prior work tackled this issue by leveraging the already learned weights and
training data for generating new weights through conducting a computationally
expensive analysis step. In this paper, we introduce MixtureGrowth, a new
approach to growing networks that circumvents the initialization overhead in
prior work. Before growing, each layer in our model is generated with a linear
combination of parameter templates. Newly grown layer weights are generated by
using a new linear combination of existing templates for a layer. On one hand,
these templates are already trained for the task, providing a strong
initialization. On the other, the new coefficients provide flexibility for the
added layer weights to learn something new. We show that our approach boosts
top-1 accuracy over the state-of-the-art by 2-2.5% on CIFAR-100 and ImageNet
datasets, while achieving comparable performance with fewer FLOPs to a larger
network trained from scratch. Code is available at
https://github.com/chaudatascience/mixturegrowth.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: EpiK-Eval: Evaluation for Language Models as Epistemic Models
Abstract: In the age of artificial intelligence, the role of large language models
(LLMs) is becoming increasingly central. Despite their growing prevalence,
their capacity to consolidate knowledge from different training documents - a
crucial ability in numerous applications - remains unexplored. This paper
presents the first study examining the capability of LLMs to effectively
combine such information within their parameter space. We introduce EpiK-Eval,
a novel question-answering benchmark tailored to evaluate LLMs' proficiency in
formulating a coherent and consistent knowledge representation from segmented
narratives. Evaluations across various LLMs reveal significant weaknesses in
this domain. We contend that these shortcomings stem from the intrinsic nature
of prevailing training objectives. Consequently, we advocate for refining the
approach towards knowledge consolidation, as it harbors the potential to
dramatically improve their overall effectiveness and performance. The findings
from this study offer insights for developing more robust and reliable LLMs.
Our code and benchmark are available at
https://github.com/chandar-lab/EpiK-Eval

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Interpretable pap smear cell representation for cervical cancer screening
Abstract: Screening is critical for prevention and early detection of cervical cancer
but it is time-consuming and laborious. Supervised deep convolutional neural
networks have been developed to automate pap smear screening and the results
are promising. However, the interest in using only normal samples to train deep
neural networks has increased owing to class imbalance problems and
high-labeling costs that are both prevalent in healthcare. In this study, we
introduce a method to learn explainable deep cervical cell representations for
pap smear cytology images based on one class classification using variational
autoencoders. Findings demonstrate that a score can be calculated for cell
abnormality without training models with abnormal samples and localize
abnormality to interpret our results with a novel metric based on absolute
difference in cross entropy in agglomerative clustering. The best model that
discriminates squamous cell carcinoma (SCC) from normals gives 0.908 +- 0.003
area under operating characteristic curve (AUC) and one that discriminates
high-grade epithelial lesion (HSIL) 0.920 +- 0.002 AUC. Compared to other
clustering methods, our method enhances the V-measure and yields higher
homogeneity scores, which more effectively isolate different abnormality
regions, aiding in the interpretation of our results. Evaluation using in-house
and additional open dataset show that our model can discriminate abnormality
without the need of additional training of deep models.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Debate Helps Supervise Unreliable Experts
Abstract: As AI systems are used to answer more difficult questions and potentially
help create new knowledge, judging the truthfulness of their outputs becomes
more difficult and more important. How can we supervise unreliable experts,
which have access to the truth but may not accurately report it, to give
answers that are systematically true and don't just superficially seem true,
when the supervisor can't tell the difference between the two on their own? In
this work, we show that debate between two unreliable experts can help a
non-expert judge more reliably identify the truth. We collect a dataset of
human-written debates on hard reading comprehension questions where the judge
has not read the source passage, only ever seeing expert arguments and short
quotes selectively revealed by 'expert' debaters who have access to the
passage. In our debates, one expert argues for the correct answer, and the
other for an incorrect answer. Comparing debate to a baseline we call
consultancy, where a single expert argues for only one answer which is correct
half of the time, we find that debate performs significantly better, with 84%
judge accuracy compared to consultancy's 74%. Debates are also more efficient,
being 68% of the length of consultancies. By comparing human to AI debaters, we
find evidence that with more skilled (in this case, human) debaters, the
performance of debate goes up but the performance of consultancy goes down. Our
error analysis also supports this trend, with 46% of errors in human debate
attributable to mistakes by the honest debater (which should go away with
increased skill); whereas 52% of errors in human consultancy are due to
debaters obfuscating the relevant evidence from the judge (which should become
worse with increased skill). Overall, these results show that debate is a
promising approach for supervising increasingly capable but potentially
unreliable AI systems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Online Vectorized HD Map Construction using Geometry
Abstract: The construction of online vectorized High-Definition (HD) maps is critical
for downstream prediction and planning. Recent efforts have built strong
baselines for this task, however, shapes and relations of instances in urban
road systems are still under-explored, such as parallelism, perpendicular, or
rectangle-shape. In our work, we propose GeMap ($\textbf{Ge}$ometry
$\textbf{Map}$), which end-to-end learns Euclidean shapes and relations of map
instances beyond basic perception. Specifically, we design a geometric loss
based on angle and distance clues, which is robust to rigid transformations. We
also decouple self-attention to independently handle Euclidean shapes and
relations. Our method achieves new state-of-the-art performance on the NuScenes
and Argoverse 2 datasets. Remarkably, it reaches a 71.8% mAP on the large-scale
Argoverse 2 dataset, outperforming MapTR V2 by +4.4% and surpassing the 70% mAP
threshold for the first time. Code is available at
https://github.com/cnzzx/GeMap

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: DDxT: Deep Generative Transformer Models for Differential Diagnosis
Abstract: Differential Diagnosis (DDx) is the process of identifying the most likely
medical condition among the possible pathologies through the process of
elimination based on evidence. An automated process that narrows a large set of
pathologies down to the most likely pathologies will be of great importance.
The primary prior works have relied on the Reinforcement Learning (RL) paradigm
under the intuition that it aligns better with how physicians perform DDx. In
this paper, we show that a generative approach trained with simpler supervised
and self-supervised learning signals can achieve superior results on the
current benchmark. The proposed Transformer-based generative network, named
DDxT, autoregressively produces a set of possible pathologies, i.e., DDx, and
predicts the actual pathology using a neural network. Experiments are performed
using the DDXPlus dataset. In the case of DDx, the proposed network has
achieved a mean accuracy of 99.82% and a mean F1 score of 0.9472. Additionally,
mean accuracy reaches 99.98% with a mean F1 score of 0.9949 while predicting
ground truth pathology. The proposed DDxT outperformed the previous RL-based
approaches by a big margin. Overall, the automated Transformer-based DDx
generative model has the potential to become a useful tool for a physician in
times of urgency.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
Abstract: Federated Learning (FL) is a decentralized machine learning paradigm that
enables collaborative model training across dispersed nodes without having to
force individual nodes to share data. However, its broad adoption is hindered
by the high communication costs of transmitting a large number of model
parameters. This paper presents EvoFed, a novel approach that integrates
Evolutionary Strategies (ES) with FL to address these challenges. EvoFed
employs a concept of 'fitness-based information sharing', deviating
significantly from the conventional model-based FL. Rather than exchanging the
actual updated model parameters, each node transmits a distance-based
similarity measure between the locally updated model and each member of the
noise-perturbed model population. Each node, as well as the server, generates
an identical population set of perturbed models in a completely synchronized
fashion using the same random seeds. With properly chosen noise variance and
population size, perturbed models can be combined to closely reflect the actual
model updated using the local dataset, allowing the transmitted similarity
measures (or fitness values) to carry nearly the complete information about the
model parameters. As the population size is typically much smaller than the
number of model parameters, the savings in communication load is large. The
server aggregates these fitness values and is able to update the global model.
This global fitness vector is then disseminated back to the nodes, each of
which applies the same update to be synchronized to the global model. Our
analysis shows that EvoFed converges, and our experimental results validate
that at the cost of increased local processing loads, EvoFed achieves
performance comparable to FedAvg while reducing overall communication
requirements drastically in various practical settings.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Online Continual Knowledge Learning for Language Models
Abstract: Large Language Models (LLMs) serve as repositories of extensive world
knowledge, enabling them to perform tasks such as question-answering and
fact-checking. However, this knowledge can become obsolete as global contexts
change. In this paper, we introduce a novel problem in the realm of continual
learning: Online Continual Knowledge Learning (OCKL). This problem formulation
aims to manage the dynamic nature of world knowledge in LMs under real-time
constraints. We propose a new benchmark and evaluation metric designed to
measure both the rate of new knowledge acquisition and the retention of
previously learned knowledge. Our empirical evaluation, conducted using a
variety of state-of-the-art methods, establishes robust base-lines for OCKL.
Our results reveal that existing continual learning approaches are
unfortunately insufficient for tackling the unique challenges posed by OCKL. We
identify key factors that influence the trade-off between knowledge acquisition
and retention, thereby advancing our understanding of how to train LMs in a
continually evolving environment.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Optimizing Inventory Routing: A Decision-Focused Learning Approach using Neural Networks
Abstract: Inventory Routing Problem (IRP) is a crucial challenge in supply chain
management as it involves optimizing efficient route selection while
considering the uncertainty of inventory demand planning. To solve IRPs,
usually a two-stage approach is employed, where demand is predicted using
machine learning techniques first, and then an optimization algorithm is used
to minimize routing costs. Our experiment shows machine learning models fall
short of achieving perfect accuracy because inventory levels are influenced by
the dynamic business environment, which, in turn, affects the optimization
problem in the next stage, resulting in sub-optimal decisions. In this paper,
we formulate and propose a decision-focused learning-based approach to solving
real-world IRPs. This approach directly integrates inventory prediction and
routing optimization within an end-to-end system potentially ensuring a robust
supply chain strategy.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Non-autoregressive Machine Translation with Probabilistic Context-free Grammar
Abstract: Non-autoregressive Transformer(NAT) significantly accelerates the inference
of neural machine translation. However, conventional NAT models suffer from
limited expression power and performance degradation compared to autoregressive
(AT) models due to the assumption of conditional independence among target
tokens. To address these limitations, we propose a novel approach called
PCFG-NAT, which leverages a specially designed Probabilistic Context-Free
Grammar (PCFG) to enhance the ability of NAT models to capture complex
dependencies among output tokens. Experimental results on major machine
translation benchmarks demonstrate that PCFG-NAT further narrows the gap in
translation quality between NAT and AT models. Moreover, PCFG-NAT facilitates a
deeper understanding of the generated sentences, addressing the lack of
satisfactory explainability in neural machine translation.Code is publicly
available at https://github.com/ictnlp/PCFG-NAT.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Adversarial Examples in the Physical World: A Survey
Abstract: Deep neural networks (DNNs) have demonstrated high vulnerability to
adversarial examples. Besides the attacks in the digital world, the practical
implications of adversarial examples in the physical world present significant
challenges and safety concerns. However, current research on physical
adversarial examples (PAEs) lacks a comprehensive understanding of their unique
characteristics, leading to limited significance and understanding. In this
paper, we address this gap by thoroughly examining the characteristics of PAEs
within a practical workflow encompassing training, manufacturing, and
re-sampling processes. By analyzing the links between physical adversarial
attacks, we identify manufacturing and re-sampling as the primary sources of
distinct attributes and particularities in PAEs. Leveraging this knowledge, we
develop a comprehensive analysis and classification framework for PAEs based on
their specific characteristics, covering over 100 studies on physical-world
adversarial examples. Furthermore, we investigate defense strategies against
PAEs and identify open challenges and opportunities for future research. We aim
to provide a fresh, thorough, and systematic understanding of PAEs, thereby
promoting the development of robust adversarial learning and its application in
open-world scenarios.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Evolving Reservoirs for Meta Reinforcement Learning
Abstract: Animals often demonstrate a remarkable ability to adapt to their environments
during their lifetime. They do so partly due to the evolution of morphological
and neural structures. These structures capture features of environments shared
between generations to bias and speed up lifetime learning. In this work, we
propose a computational model for studying a mechanism that can enable such a
process. We adopt a computational framework based on meta reinforcement
learning as a model of the interplay between evolution and development. At the
evolutionary scale, we evolve reservoirs, a family of recurrent neural networks
that differ from conventional networks in that one optimizes not the weight
values but hyperparameters of the architecture: the later control macro-level
properties, such as memory and dynamics. At the developmental scale, we employ
these evolved reservoirs to facilitate the learning of a behavioral policy
through Reinforcement Learning (RL). Within an RL agent, a reservoir encodes
the environment state before providing it to an action policy. We evaluate our
approach on several 2D and 3D simulated environments. Our results show that the
evolution of reservoirs can improve the learning of diverse challenging tasks.
We study in particular three hypotheses: the use of an architecture combining
reservoirs and reinforcement learning could enable (1) solving tasks with
partial observability, (2) generating oscillatory dynamics that facilitate the
learning of locomotion tasks, and (3) facilitating the generalization of
learned behaviors to new tasks unknown during the evolution phase.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Improving Real Estate Appraisal with POI Integration and Areal Embedding
Abstract: Despite advancements in real estate appraisal methods, this study primarily
focuses on two pivotal challenges. Firstly, we explore the often-underestimated
impact of Points of Interest (POI) on property values, emphasizing the
necessity for a comprehensive, data-driven approach to feature selection.
Secondly, we integrate road-network-based Areal Embedding to enhance spatial
understanding for real estate appraisal. We first propose a revised method for
POI feature extraction, and discuss the impact of each POI for house price
appraisal. Then we present the Areal embedding-enabled Masked Multihead
Attention-based Spatial Interpolation for House Price Prediction (AMMASI)
model, an improvement upon the existing ASI model, which leverages masked
multi-head attention on geographic neighbor houses and similar-featured houses.
Our model outperforms current baselines and also offers promising avenues for
future optimization in real estate appraisal methodologies.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Tasks
Abstract: Instruction tuning (IT) achieves impressive zero-shot generalization results
by training large language models (LLMs) on a massive amount of diverse tasks
with instructions. However, how to select new tasks to improve the performance
and generalizability of IT models remains an open question. Training on all
existing tasks is impractical due to prohibiting computation requirements, and
randomly selecting tasks can lead to suboptimal performance. In this work, we
propose active instruction tuning based on prompt uncertainty, a novel
framework to identify informative tasks, and then actively tune the models on
the selected tasks. We represent the informativeness of new tasks with the
disagreement of the current model outputs over perturbed prompts. Our
experiments on NIV2 and Self-Instruct datasets demonstrate that our method
consistently outperforms other baseline strategies for task selection,
achieving better out-of-distribution generalization with fewer training tasks.
Additionally, we introduce a task map that categorizes and diagnoses tasks
based on prompt uncertainty and prediction probability. We discover that
training on ambiguous (prompt-uncertain) tasks improves generalization while
training on difficult (prompt-certain and low-probability) tasks offers no
benefit, underscoring the importance of task selection for instruction tuning.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Can Large Language Models Augment a Biomedical Ontology with missing Concepts and Relations?
Abstract: Ontologies play a crucial role in organizing and representing knowledge.
However, even current ontologies do not encompass all relevant concepts and
relationships. Here, we explore the potential of large language models (LLM) to
expand an existing ontology in a semi-automated fashion. We demonstrate our
approach on the biomedical ontology SNOMED-CT utilizing semantic relation types
from the widely used UMLS semantic network. We propose a method that uses
conversational interactions with an LLM to analyze clinical practice guidelines
(CPGs) and detect the relationships among the new medical concepts that are not
present in SNOMED-CT. Our initial experimentation with the conversational
prompts yielded promising preliminary results given a manually generated gold
standard, directing our future potential improvements.

Output: Computational Linguistics