42 citations · 81 across the 24 of their papers we have counts for
25 papers
Encoded but Unused: Bounding the Sources of VLM Errors
Anat Veikherman, Mor Ventura, Nitay Calderon +2
In this paper, we investigate why vision-language models (VLMs) produce errors by decomposing them into two sources: visual-representation failures, in which the visual evidence re…
CT: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees
S M Rafiuddin, Atriya Sen
Sentiment in social-media threads does not only vary across posts; it shifts as users react to claims, corrections, evidence, and hostility within a branching reply tree. We study…
Motivation in Large Language Models
Omer Nahum, Asael Sklar, Ariel Goldstein +1
Motivation is a central driver of human behavior, shaping decisions, goals, and task performance. As large language models (LLMs) become increasingly aligned with human preferences…
Thinking to Recall: How Reasoning Unlocks Parametric Knowledge in LLMs
Zorik Gekhman, Roee Aharoni, Eran Ofek +3
While reasoning in LLMs plays a natural role in math, code generation, and multi-hop factual questions, its effect on simple, single-hop factual questions remains unclear. Such que…
HACK: Hallucinations Along Certainty and Knowledge Axes
Adi Simhi, Jonathan Herzig, Itay Itzhak +7
Hallucinations in LLMs present a critical barrier to their reliable usage. Existing research usually categorizes hallucination by their external properties rather than by the LLMs'…
DeLeaker: Dynamic Inference-Time Reweighting For Semantic Leakage Mitigation in Text-to-Image Models
Mor Ventura, Michael Toker, Or Patashnik +2
Text-to-Image (T2I) models have advanced rapidly, yet they remain vulnerable to semantic leakage, the unintended transfer of semantically related features between distinct entities…
Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs
Yehonatan Peisakhovsky, Zorik Gekhman, Yosi Mass +2
Context-grounded hallucinations are cases where model outputs contain information not verifiable against the source text. We study the applicability of LLMs for localizing such hal…
Leveraging NTPs for Efficient Hallucination Detection in VLMs
Ofir Azachi, Kfir Eliyahu, Eyal El Ani +4
Hallucinations of vision-language models (VLMs), which are misalignments between visual content and generated text, undermine the reliability of VLMs. One common approach for detec…
Bridging Online Behavior and Clinical Insight: A Longitudinal LLM-based Study of Suicidality on YouTube Reveals Novel Digital Markers
Ilanit Sobol, Shir Lissak, Refael Tikochinski +4
Suicide remains a leading cause of death in Western countries. As social media becomes central to daily life, digital footprints offer valuable insight into suicidal behavior. Focu…
Multi-Domain Explainability of Preferences
Nitay Calderon, Liat Ein-Dor, Roi Reichart
Preference mechanisms, such as human preference, LLM-as-a-Judge (LaaJ), and reward models, are central to aligning and evaluating large language models (LLMs). Yet, the underlying…
Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awareness
Lotem Peled-Cohen, Maya Zadok, Nitay Calderon +2
Cognitive decline often surfaces in language years before diagnosis. It is frequently non-experts, such as those closest to the patient, who first sense a change and raise concern.…
The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs
Nitay Calderon, Roi Reichart, Rotem Dror
The "LLM-as-an-annotator" and "LLM-as-a-judge" paradigms employ Large Language Models (LLMs) as annotators, judges, and evaluators in tasks traditionally performed by humans. LLM a…
NL-Eye: Abductive NLI for Images
Mor Ventura, Michael Toker, Nitay Calderon +3
Will a Visual Language Model (VLM)-based bot warn us about slipping if it detects a wet floor? Recent VLMs have demonstrated impressive capabilities, yet their ability to infer out…
Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance
Omer Nahum, Nitay Calderon, Orgad Keller +2
NLP benchmarks rely on standardized datasets for training and evaluating models and are crucial for advancing the field. Traditionally, expert annotations ensure high-quality label…
LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations
Hadas Orgad, Michael Toker, Zorik Gekhman +4
Large language models (LLMs) often produce errors, including factual inaccuracies, biases, and reasoning failures, collectively referred to as "hallucinations". Recent studies have…
A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunities
Lotem Peled-Cohen, Roi Reichart
The close link between cognitive decline and language has fostered long-standing collaboration between the NLP and medical communities in dementia research. To examine this, we rev…
On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs
Nitay Calderon, Roi Reichart
Recent advancements in NLP systems, particularly with the introduction of LLMs, have led to widespread adoption of these systems by a broad spectrum of users across various domains…
Can LLMs Learn Macroeconomic Narratives from Social Media?
Almog Gueta, Amir Feder, Zorik Gekhman +2
This study empirically tests the hypothesis, which posits that narratives (ideas that are spread virally and affect public beliefs) can influence eco…
Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
Zorik Gekhman, Gal Yona, Roee Aharoni +4
When large language models are aligned via supervised fine-tuning, they may encounter new factual information that was not acquired through pre-training. It is often conjectured th…
Bored to Death: Artificial Intelligence Research Reveals the Role of Boredom in Suicide Behavior
Shir Lissak, Yaakov Ophir, Refael Tikochinski +4
Background: Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is…
The Colorful Future of LLMs: Evaluating and Improving LLMs as Emotional Supporters for Queer Youth
Shir Lissak, Nitay Calderon, Geva Shenkman +4
Queer youth face increased mental health risks, such as depression, anxiety, and suicidal ideation. Hindered by negative stigma, they often avoid seeking help and rely on online re…
Systematic Biases in LLM Simulations of Debates
Amir Taubenfeld, Yaniv Dover, Roi Reichart +1
The emergence of Large Language Models (LLMs), has opened exciting possibilities for constructing computational simulations designed to replicate human behavior accurately. Current…
Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games
Eilam Shapira, Omer Madmon, Roi Reichart +1
Human choice prediction in economic contexts is crucial for applications in marketing, finance, public policy, and more. This task, however, is often constrained by the difficultie…
Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals
Yair Gat, Nitay Calderon, Amir Feder +3
Causal explanations of the predictions of NLP systems are essential to ensure safety and establish trust. Yet, existing methods often fall short of explaining model predictions eff…