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Newest papers, page 104
HeRE: A Novel Benchmark for Hebrew Relation Extraction via LLM-Guided Denoising of Knowledge Graph Alignments
While LLMs demonstrate strong capabilities in English Relation Extraction (RE) tasks, a significant gap remains in assessing their RE performance in low-resource languages, due to a lack of…Cultural (In)Competence: Investigating Culture-Specific Stereotype Detection Capabilities of LLMs
Stereotypes are subjective constructs that are mediated by the cultural context in which they are viewed.Who Do Perspectivist Models Predict Well? Annotator Agreement, Sparsity, and Performance
Successful completion of perspectivist natural language processing (NLP) annotation tasks requires algorithms to be aligned across diverse perspectives in order to predict how different humans will…Repairing LLM-Generated Specifications with Deterministic Diagnosis
Large language models (LLMs) are increasingly used as formalizers: given a reasoning problem expressed in natural language, they produce a formal specification that a symbolic solver can then solve…Mitigating Demographic Disparities in Explanation-Human Alignment via Rationale Regularization
We study explanation fairness in hate speech detection.SAG2: Embedding-Free Multi-Hop Retrieval with BM25 and JEV
Strong multi-hop retrieval should be practical to deploy without expensive corpus preparation or complex database infrastructure.The Model Knows Better: Defending Medical Multi-Agent Systems Against Prompt Injection
Medical multi-agent systems, in which LLM agents discuss a case, are increasingly studied for clinical decision support.Broad and Efficient PII Detection with Unified Labels
Personally Identifiable Information (PII) detection models must efficiently find identifiers across diverse contexts while maintaining a low false-positive rate (FPR) on high-volume traffic.Can LLMs predict the outcomes of machine learning experiments?
Recent advances in AI make Recursive Self-Improvement (RSI) increasingly plausible.When Does Uncertainty Measure RAG Hallucination Risk?
Uncertainty can measure hallucination risk in retrieval-augmented generation (RAG), but only when retrieved evidence constrains the answer.Trajectory-Guided Steering: Mitigating Spurious Feature Bias in Clinical Classification
Large language models rely on spurious features in clinical classification: swapping a single gender token in clinical notes alters gender-irrelevant diagnoses in up to 53.5% of cases.A Plausible Explanation Is Enough to Mislead Multimodal Judges
Multimodal large language models (MLLMs) increasingly serve as judges in training and evaluation pipelines, often assessing answers together with explanations.ToMStories: Interpreting Theory-of-Mind in Synthetic Narratives
Understanding people often requires inferring what they feel or want from how they behave rather than from what they state outright.Measuring Mechanistic Uncertainty in Large Language Models via Causal Robustness
Large language models (LLMs) are increasingly used in critical domains, where their susceptibility to hallucinations makes reliable and interpretable uncertainty quantification essential.Inductive Learning of Concept Embeddings from Cross-Linguistic Colexification Patterns with Textual Attributes
The advent of distributional semantics as a framework for computational semantics marked a paradigm shift in the nature of representations, by which meanings are represented as dense vectors…SAFETYEDIT: Sparse Internal Targets with Prompt-Wide Control for LLM Jailbreak Backdoors
Model-editing backdoors aim to induce harmful responses under a trigger while preserving ordinary behavior on untriggered input.Evidence-Gated Bilingual RAG for Reliable Consumer Banking Search
Consumers often describe what they need rather than use the terminology found on a banking website.Secondhand Stress: How Stressful Disclosures Affect Psychosocial Wellbeing in Online Graduate Admissions Communities
Online communities provide spaces for individuals navigating stressful life transitions to disclose difficulties and seek peer support.Exploring The Empirical Privacy Landscape of Synthetic Data
Methods for training language models on sensitive data differ in how they balance capability gains against the risk of leaking private information.ProPerAgent: Toward Memory-Driven Proactive Assistance
As assistants move from reactive answering toward proactive intervention, memory changes role: it no longer only supplies the answer to a query, it determines whether and when to speak at all.