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View article: Application of machine learning and temporal response function modeling of EEG data for differential diagnosis in primary progressive aphasia
Application of machine learning and temporal response function modeling of EEG data for differential diagnosis in primary progressive aphasia Open
Primary progressive aphasia (PPA) is a neurodegenerative syndrome characterized by progressive decline in speech and/or language. There are three PPA subtypes with distinct speech-language profiles. Early diagnosis is essential for optimal…
View article: Development and classification accuracy of an automated cognitive screening tool combining working memory and connected speech tasks for early detection of cognitive impairment in primary care
Development and classification accuracy of an automated cognitive screening tool combining working memory and connected speech tasks for early detection of cognitive impairment in primary care Open
INTRODUCTION Cognitive screening to detect mild cognitive impairment (MCI) and dementia in primary care settings has proven to be a challenging task. The ideal solution would be a brief, yet sensitive, tool appropriate for use with individ…
View article: Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine
Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine Open
Technological progress has led to concrete advancements in tasks that were regarded as challenging, such as automatic fact-checking. Interest in adopting these systems for public health and medicine has grown due to the high-stakes nature …
View article: A Tool for Generating Exceptional Behavior Tests With Large Language Models
A Tool for Generating Exceptional Behavior Tests With Large Language Models Open
Exceptional behavior tests (EBTs) are crucial in software development for verifying that code correctly handles unwanted events and throws appropriate exceptions. However, prior research has shown that developers often prioritize testing "…
View article: Strategic Discourse Assessment: The Crooked Path to Innocence
Strategic Discourse Assessment: The Crooked Path to Innocence Open
Language is often used strategically, particularly in high-stakes, adversarial settings, yet most work on pragmatics and LLMs centers on cooperativity. This leaves a gap in the systematic understanding of strategic communication in adversa…
View article: AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy
AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy Open
Large Language Models (LLMs) are being explored for applications in scientific research, including their capabilities to synthesize literature, answer research questions, generate research ideas, and even conduct computational experiments.…
View article: QUDsim: Quantifying Discourse Similarities in LLM-Generated Text
QUDsim: Quantifying Discourse Similarities in LLM-Generated Text Open
As large language models become increasingly capable at various writing tasks, their weakness at generating unique and creative content becomes a major liability. Although LLMs have the ability to generate text covering diverse topics, the…
View article: Behavioral Analysis of Information Salience in Large Language Models
Behavioral Analysis of Information Salience in Large Language Models Open
Large Language Models (LLMs) excel at text summarization, a task that requires models to select content based on its importance. However, the exact notion of salience that LLMs have internalized remains unclear. To bridge this gap, we intr…
View article: Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?
Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature? Open
Medical research faces well-documented challenges in translating novel treatments into clinical practice. Publishing incentives encourage researchers to present "positive" findings, even when empirical results are equivocal. Consequently, …
View article: SPRI: Aligning Large Language Models with Context-Situated Principles
SPRI: Aligning Large Language Models with Context-Situated Principles Open
Aligning Large Language Models to integrate and reflect human values, especially for tasks that demand intricate human oversight, is arduous since it is resource-intensive and time-consuming to depend on human expertise for context-specifi…
View article: QuaLLM-Health: An Adaptation of an LLM-Based Framework for Quantitative Data Extraction from Online Health Discussions
QuaLLM-Health: An Adaptation of an LLM-Based Framework for Quantitative Data Extraction from Online Health Discussions Open
Health-related discussions on social media like Reddit offer valuable insights, but extracting quantitative data from unstructured text is challenging. In this work, we present an adapted framework from QuaLLM into QuaLLM-Health for extrac…
View article: Learning to Refine with Fine-Grained Natural Language Feedback
Learning to Refine with Fine-Grained Natural Language Feedback Open
Recent work has explored the capability of large language models (LLMs) to identify and correct errors in LLM-generated responses. These refinement approaches frequently evaluate what sizes of models are able to do refinement for what prob…
View article: Detection and Measurement of Syntactic Templates in Generated Text
Detection and Measurement of Syntactic Templates in Generated Text Open
Recent work on evaluating the diversity of text generated by LLMs has focused on word-level features. Here we offer an analysis of syntactic features to characterize general repetition in models, beyond frequent n-grams. Specifically, we d…
View article: Do they mean 'us'? Interpreting Referring Expressions in Intergroup Bias
Do they mean 'us'? Interpreting Referring Expressions in Intergroup Bias Open
The variations between in-group and out-group speech (intergroup bias) are subtle and could underlie many social phenomena like stereotype perpetuation and implicit bias. In this paper, we model the intergroup bias as a tagging task on Eng…
View article: Which questions should I answer? Salience Prediction of Inquisitive Questions
Which questions should I answer? Salience Prediction of Inquisitive Questions Open
Inquisitive questions -- open-ended, curiosity-driven questions people ask as they read -- are an integral part of discourse processing (Kehler and Rohde, 2017; Onea, 2016) and comprehension (Prince, 2004). Recent work in NLP has taken adv…
View article: Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided
Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided Open
Large language models (LLMs) have offered new opportunities for emotional support, and recent work has shown that they can produce empathic responses to people in distress. However, long-term mental well-being requires emotional self-regul…
View article: Large Language Models Produce Responses Perceived to be Empathic
Large Language Models Produce Responses Perceived to be Empathic Open
Large Language Models (LLMs) have demonstrated surprising performance on many tasks, including writing supportive messages that display empathy. Here, we had these models generate empathic messages in response to posts describing common li…
View article: FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence Open
Plain language summarization with LLMs can be useful for improving textual accessibility of technical content. But how factual are these summaries in a high-stakes domain like medicine? This paper presents FactPICO, a factuality benchmark …
View article: InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification Open
Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. This work proposes InfoLossQA, a framework to characterize and recover simplification-induced informa…
View article: Multilingual Code Co-evolution using Large Language Models
Multilingual Code Co-evolution using Large Language Models Open
Many software projects implement APIs and algorithms in multiple programming languages. Maintaining such projects is tiresome, as developers have to ensure that any change (e.g., a bug fix or a new feature) is being propagated, timely and …
View article: Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion
Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion Open
Situations and events evoke emotions in humans, but to what extent do they inform the prediction of emotion detection models? This work investigates how well human-annotated emotion triggers correlate with features that models deemed salie…
View article: QUDEVAL: The Evaluation of Questions Under Discussion Discourse Parsing
QUDEVAL: The Evaluation of Questions Under Discussion Discourse Parsing Open
Questions Under Discussion (QUD) is a versatile linguistic framework in which discourse progresses as continuously asking questions and answering them. Automatic parsing of a discourse to produce a QUD structure thus entails a complex ques…