5 papers
NOVA: NOise-aware Verbal Confidence CAlibration for Robust Large Language Models in RAG Systems
Jiayu Liu, Rui Wang, Qing Zong +9
Accurately assessing model confidence is essential for deploying large language models (LLMs) in mission-critical factual domains. While retrieval-augmented generation (RAG) is wid…
Diversity-Enhanced Reasoning for Subjective Questions
Yumeng Wang, Zhiyuan Fan, Jiayu Liu +2
Large Reasoning Models (LRMs) with long chain-of-thought capabilities, optimized via reinforcement learning with verifiable rewards (RLVR), excel at objective reasoning tasks like…
Mathematical Proof as a Litmus Test: Revealing Failure Modes of Advanced Large Reasoning Models
Dadi Guo, Jiayu Liu, Zhiyuan Fan +5
Large reasoning models (e.g., R1, o3) have demonstrated remarkable mathematical problem-solving abilities. However, the high reported accuracy of these advanced models on popular d…
Unveiling the Lack of LVLM Robustness to Fundamental Visual Variations: Why and Path Forward
Zhiyuan Fan, Yumeng Wang, Sandeep Polisetty +1
Large Vision Language Models (LVLMs) excel in various vision-language tasks. Yet, their robustness to visual variations in position, scale, orientation, and context that objects in…
CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering
Yumeng Wang, Zhiyuan Fan, Qingyun Wang +2
Large Language Models (LLMs) are pretrained on extensive multilingual corpora to acquire both language-specific cultural knowledge and general knowledge. Ideally, while LLMs should…