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9,261 papers
Large language models (LLMs) are increasingly used to answer everyday health questions online, including whether someone can take another dose of an over-the-counter (OTC) medication. Yet this common safety-relevant setting remains underexplored in medical QA. Answering these questions requires checking previous doses,...
OP-Mix (On-Policy Mix), a data mixing algorithm that operates across pretraining, midtraining, and instruction tuning, is introduced, suggesting a different view of language model training: not a sequence of distinct phases, but a single continuous process of learning from data.
Recent advances in large language models (LLMs) have led to the development of AI-powered tutoring systems that provide interactive support via dialogue. To enable these tutoring systems to provide personalized support, it is essential to assess student performance at each turn, motivating knowledge tracing (KT) in dia...
Multi-hop QA benchmarks often reward Large Language Models (LLMs) for spurious correctness, where models reach correct answers through invalid intermediate reasoning. We propose SAFE, an LLM-as-verifier framework for evidence-grounded multi-hop QA. Rather than judging only the final answer after generation, SAFE verifi...
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It is shown that the transition from productive to redundant reasoning is decodable from late-layer hidden states and representations separate the two phases around first-correct-solution boundaries beyond what position and lexical controls explain, and distinguish correct from incorrect solution endings.
While Multimodal Large Language Models (MLLMs) show promising performance in automated electrocardiogram interpretation, it remains unclear whether they perform actual step-by-step reasoning or just rely on superficial visual cues. To investigate this, we introduce ECG-Reasoning-Benchmark, a novel multi-turn evaluation...
Large language models routinely produce factually incorrect or misleading outputs, yet conventional automated benchmarks overlook how users perceive reliability during interaction. We introduce the System Hallucination Scale (SHS), a lightweight instrument modeling the famous System Usability Scale (SUS) and System Cau...
Anagent, a multi-agent framework for enhanced scientific table \&figure analysis through four specialized agents that leverage supervised finetuning and specialized reinforcement learning to optimize individual capabilities while maintaining effective collaboration, is proposed.
Generative AI and misinformation research have evolved since our 2024 survey. This paper presents an updated perspective, transitioning from literature review to practical countermeasures. We report on changes in the threat landscape, including improved AI-generated content through Large Language Models (LLMs) and mult...
This work proposes Jacobian Scopes, a suite of gradient-based, token level causal attribution methods for interpreting LLM predictions that reveal implicit political biases, uncover word- and phrase-level translation strategies, and shed light on recently debated mechanisms underlying in-context time-series forecasting...
This work proposes a two stage detection framework that first identifies suspicious conversations using a tailored ensemble classification model and compares its modular approach against a Dual LLM baseline that performs detection and judgment using different language models.
SSM-Informed Softmax Attention is proposed, which builds Mamba-3-style state transitions, rotation included, directly into the scores alongside FoX's gate and outperforms parameter-matched Transformer and FoX in language modeling and length extrapolation.
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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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A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
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