1 citations · 6 across the 14 of their papers we have counts for
11 papers · 1 filter
RAR: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrieval
Kaishuai Xu, Wenjun Hou, Yi Cheng +1
Large Language Models (LLMs) have shown promising performance on diverse medical benchmarks, highlighting their potential in supporting real-world clinical tasks. Retrieval-Augment…
Learning to Align Multi-Faceted Evaluation: A Unified and Robust Framework
Kaishuai Xu, Tiezheng Yu, Wenjun Hou +6
Large Language Models (LLMs) are being used more and more extensively for automated evaluation in various scenarios. Previous studies have attempted to fine-tune open-source LLMs t…
Subtle Errors in Reasoning: Preference Learning via Error-injected Self-editing
Kaishuai Xu, Tiezheng Yu, Wenjun Hou +7
Large Language Models (LLMs) have exhibited strong mathematical reasoning prowess, tackling tasks ranging from basic arithmetic to advanced competition-level problems. However, fre…
Integrative Decoding: Improve Factuality via Implicit Self-consistency
Yi Cheng, Xiao Liang, Yeyun Gong +11
Self-consistency-based approaches, which involve repeatedly sampling multiple outputs and selecting the most consistent one as the final response, prove to be remarkably effective…
Reasoning Like a Doctor: Improving Medical Dialogue Systems via Diagnostic Reasoning Process Alignment
Kaishuai Xu, Yi Cheng, Wenjun Hou +2
Medical dialogue systems have attracted significant attention for their potential to act as medical assistants. Enabling these medical systems to emulate clinicians' diagnostic rea…
No Two Devils Alike: Unveiling Distinct Mechanisms of Fine-tuning Attacks
Chak Tou Leong, Yi Cheng, Kaishuai Xu +3
The existing safety alignment of Large Language Models (LLMs) is found fragile and could be easily attacked through different strategies, such as through fine-tuning on a few harmf…