1 citations · 6 across the 14 of their papers we have counts for
14 papers
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…
RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection
Wenjun Hou, Yi Cheng, Kaishuai Xu +4
Large language models (LLMs) have demonstrated remarkable capabilities in various domains, including radiology report generation. Previous approaches have attempted to utilize mult…
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…
Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquiry
Wenjun Hou, Yi Cheng, Kaishuai Xu +3
Comprehensively understanding surgical scenes in Surgical Visual Question Answering (Surgical VQA) requires reasoning over multiple objects. Previous approaches address this task u…
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…
ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation
Wenjun Hou, Yi Cheng, Kaishuai Xu +3
Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated reports. In this paper, we emphasize anothe…
Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue
Jian Wang, Chak Tou Leong, Jiashuo Wang +3
Tuning language models for dialogue generation has been a prevalent paradigm for building capable dialogue agents. Yet, traditional tuning narrowly views dialogue generation as res…
Medical Dialogue Generation via Intuitive-then-Analytical Differential Diagnosis
Kaishuai Xu, Wenjun Hou, Yi Cheng +2
Medical dialogue systems have attracted growing research attention as they have the potential to provide rapid diagnoses, treatment plans, and health consultations. In medical dial…
COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goal
Yi Cheng, Wenge Liu, Jian Wang +5
In recent years, there has been a growing interest in exploring dialogues with more complex goals, such as negotiation, persuasion, and emotional support, which go beyond tradition…
How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation
Yang Xiao, Yi Cheng, Jinlan Fu +3
In recent years, AI has demonstrated remarkable capabilities in simulating human behaviors, particularly those implemented with large language models (LLMs). However, due to the la…
RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning
Wenjun Hou, Yi Cheng, Kaishuai Xu +2
Automating radiology report generation can significantly alleviate radiologists' workloads. Previous research has primarily focused on realizing highly concise observations while n…