Large Language Models (LLMs) frequently encounter conflicts between internal parametric knowledge and external contextual knowledge, yet the mechanisms governing this selection remain unclear. We show that the choice between context and parametric knowledge is linearly represented and can be controlled along a single d...
Yibin Lou, Yi Hu, Jian-Hui Chen et al.· 0 citations
Ambiguous inputs can admit multiple interpretations, each potentially yielding a different answer. Existing work largely treats this ambiguity as a property of the input, yet for a particular language model, only some alternatives may be known and answerable. We therefore distinguish world ambiguity, capturing all alte...
Jingqin Tang, Tim Tomov, Sebastian Schmidt et al.· 0 citations
In recent years, with advances in multimodal interaction technologies and the growing demand for intelligent emotional support, Multimodal Emotional Support Conversation (MESC) has emerged as an important task. However, existing systems still suffer from shallow responses and inappropriate support strategies due to wea...
Hang-Ke Ren, Pei-Dong Wang, Yi-Chen Gao et al.· 0 citations
Legal-content reproduction requires matching the source exactly in section hierarchy, verbatim text, citations, and amendment spans; a dropped citation or mislabeled amendment carries into the products lawyers rely on. We report an industrial pipeline converting U.S. state legislation into strict, schema-constrained XM...
GURI (Gray-box Uncertainty Representations with Identity), a lightweight gray-box hallucination detector that combines candidate-token probabilities with per-token vector identifiers that exposes which alternatives the model considered while preserving the accessibility and transferability of the gray-box setting.
S. Goldshtein, Yitzchak Grunbaum, Fabrizio Frasca et al.· 0 citations
Law students are examined through open-text answers: case solutions, essays and memos that apply the law to a set of facts. Such answers are expensive to grade, human graders disagree widely on them, and large language models (LLMs) now offer to grade them at scale, both in teaching and in the benchmarks that test lega...
Within the most recent two decades, machine translation (MT) has been advanced from a research area to a mature, deployed technology for prose text and most language pairs. However, language has different dimensions of meaning, and the alluded progress has primarily been demonstrated for propositional meaning. In this...
Silvia Gargova, Tim Menzner, Jochen L. Leidner· 0 citations
Weight pruning can degrade the accuracy of large reasoning models (LRMs), yet it remains unclear whether it damages reasoning itself. We find that much of this degradation stems from a failure to stop rather than to reason. As sparsity increases, pruned LRMs increasingly fail to terminate within the token budget, often...
Minh-Anh Nguyen, Nguyen T. Ha Lan, Xiyan Fu et al.· 0 citations
Inspired by Kahneman's account of fast and slow thinking, MemDuet is a dual-process memory framework that learns to coordinate a small language model for fast, parallel evidence selection from raw context chunks with a large language model for memory-update verification and memory updates in document order.
Jiang-Cheng Song, Hao-Dong Jing, Yu-Ling Mu et al.· 0 citations
Rubric-based evaluation assesses the responses of large language models (LLMs) by aggregating the verification results of explicit criteria, yet not all criteria matter equally. However, existing methods commonly aggregate the verification results with uniform weighting, which assigns minor and overlapping criteria the...
Haocheng Yang, Licheng Pan, Yiming Dong et al.· 0 citations
LLMs have achieved strong performance on chemical prediction benchmarks. However, existing evaluation methods typically provide target entity identity information directly, making it difficult to determine whether model predictions arise from transferable structure-property reasoning capabilities or rely on target-rela...
Yan-Yi Su, Yu Zeng, Hong-Shuai Wang et al.· 0 citations
Majority Voting (MV) is a simple and widely used strategy which improves the reasoning accuracy of Large Language Models (LLMs) by allocating additional inference-time computation. Specifically, in MV multiple Chain-of-Thoughts (CoTs) are independently sampled and the most frequent final answer is selected. In this wor...
Fiorenzo Parascandolo, Enver Sangineto, Xiaoyi Yu et al.· 0 citations
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.
