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9,568 papers
AraDynFact is introduced, a novel dynamic evaluation framework designed to rigorously assess the factual Arabic knowledge embedded in LLMs, and employs a dynamic approach to extract factual information and generate rich and answerable questions in a fast and automatic way.
TRACE is proposed, a single-pass, decoded-answer-preserving confidence estimator that treats decoding-time uncertainty as a trajectory through three steps: recording token-level surprisal and predictive entropy during decoding, applying local risk operators to preserve uncertainty spikes, and converting localized trace...
It is shown that LLMs still have a limited ability to simulate learner evaluations, and the need to investigate which learner information and adaptation strategies are effective for learner preference simulation is highlighted.
Diverse methods of machine and computational modeling have demonstrated their effectiveness in tasks of automatic clustering and classification of brain states, the development of multilevel models and the identification of connectivity patterns correlated with levels of consciousness.
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Functional interpretation is introduced, which characterizes an SAE feature as a mapping from its activating input semantics to its output effects under intervention, and Dual-End Agentic Feature Interpretation (DAFI) is presented, an agent that actively gathers evidence and refines input-side, output-side, and functio...
Large Language Models (LLMs) perform well on reasoning benchmarks, but it remains unclear whether this reflects genuine contextual reasoning or reliance on facts memorized in their parameters. We investigate this by distinguishing two possibilities: a broad \textit{memorization bias}, where familiar content improves re...
Large language models treat conversation history as unverified context, so false premises injected into earlier turns can be adopted as fact later, a failure mode we call session-level contamination. We introduce five contamination protocols along a source-authority gradient, each holding the false premise constant whi...
Aspect-based sentiment analysis (ABSA) has largely turned to text generation. We show that competitive dimensional ABSA does not need it. Using Jev, a frozen model that answers typed questions with rubric scores, label probabilities, and yes/no judgments, we decompose all three tasks of SemEval-2026 Task III Track A in...
Multi-agent large language model (LLM) debate is often evaluated by whether final answers improve, but movement is not necessarily improvement: the same discussion can rescue an initially wrong majority or destroy an initially correct one. Standard final-accuracy evaluations conflate these opposing mechanisms. We intro...
Despite substantial progress across downstream applications, vision-language models (VLMs) remain susceptible to language bias, often prioritizing linguistic cues over visual evidence and consequently producing incorrect predictions. Prior studies have proposed various approaches to understanding and mitigating languag...
GRASP is a rubric-aware on-policy self-distillation framework for LLM personalization that turns user-specific rubric aspects into fine-grained, token-level supervision, and Rubric-based Teacher Validation (RTV) is introduced, which retains only instances where the teacher sufficiently covers the target aspects, improv...
Large Language Models (LLMs) offer a scalable way to simulate survey respondents using demographic profiles and observed reference responses. However, the conventional approach of predicting one question per prompt repeatedly encodes the same context, limits each target to a narrow set of reference responses, and preve...
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Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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