1 citations · 1 across the 2 of their papers we have counts for
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Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability
Zicheng Lin, Tian Liang, Jiahao Xu +7
Mathematical reasoning tasks pose significant challenges for large language models (LLMs) because they require precise logical deduction and sequence analysis. In this work, we int…
Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training
Youliang Yuan, Wenxiang Jiao, Wenxuan Wang +5
This study addresses a critical gap in safety tuning practices for Large Language Models (LLMs) by identifying and tackling a refusal position bias within safety tuning data, which…
CriticBench: Benchmarking LLMs for Critique-Correct Reasoning
Zicheng Lin, Zhibin Gou, Tian Liang +3
The ability of Large Language Models (LLMs) to critique and refine their reasoning is crucial for their application in evaluation, feedback provision, and self-improvement. This pa…
Leveraging Word Guessing Games to Assess the Intelligence of Large Language Models
Tian Liang, Zhiwei He, Jen-tse Huang +7
The automatic evaluation of LLM-based agent intelligence is critical in developing advanced LLM-based agents. Although considerable effort has been devoted to developing human-anno…
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
Tian Liang, Zhiwei He, Wenxiang Jiao +6
Modern large language models (LLMs) like ChatGPT have shown remarkable performance on general language tasks but still struggle on complex reasoning tasks, which drives the researc…
Exploring Human-Like Translation Strategy with Large Language Models
Zhiwei He, Tian Liang, Wenxiang Jiao +6
Large language models (LLMs) have demonstrated impressive capabilities in general scenarios, exhibiting a level of aptitude that approaches, in some aspects even surpasses, human-l…