1 citations · 1 across the 12 of their papers we have counts for
12 papers
CDEG: Learning Decision-Critical Evidence for Long-Horizon Diagnostic Agents
Xiwei Dai, Zijie Meng, Zhiting Fan +4
Unlike static medical question answering, long-horizon diagnosis captures the sequential nature of clinical practice: evidence is progressively acquired, integrated, and evaluated…
BridgeAlign: Bridging Preference Alignment for Humanities and Social Sciences
Ru Peng, Haokai Xu, Xijun Gu +11
While data synthesis for large language models (LLMs) is prevalent, it primarily targets domains with verifiable answers, overlooking open-ended humanities and social sciences (HSS…
HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs
Ru Peng, Tianyu Zhao, Xijun Gu +9
High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. Data synthesis is a viable alternative and succeeds on closed tasks, yet the hum…
LongMedBench: Benchmarking Medical Agents for Long-Horizon Clinical Decision-Making
Zihan Xu, Yanzhen Chen, Xiaocheng Zhang +4
In this work, we introduce LongMedBench, a real-world EHR-based benchmark for long-horizon clinical decision-making. Prior evaluations of LLM-based medical agents have largely emph…
Med-R2: An Adversarial Benchmark for Evidence-Grounded Reasoning in Medical VLMs
Wen Ma, Fucheng Niu, Zhiting Fan +3
Vision-language models have demonstrated impressive capabilities in general medical visual question answering, yet due to limited interpretability, it remains unclear whether their…
Optimsyn: Influence-Guided Rubrics Optimization for Synthetic Data Generation
Zhiting Fan, Ruizhe Chen, Tianxiang Hu +7
Large language models (LLMs) achieve strong downstream performance largely due to abundant supervised fine-tuning (SFT) data. However, high-quality SFT data in knowledge-intensive…
Reinforcement Learning with Rubric Anchors
Zenan Huang, Yihong Zhuang, Guoshan Lu +18
Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing Large Language Models (LLMs), exemplified by the success of OpenAI's o-series…
Datasets and Recipes for Video Temporal Grounding via Reinforcement Learning
Ruizhe Chen, Zhiting Fan, Tianze Luo +7
Video Temporal Grounding (VTG) aims to localize relevant temporal segments in videos given natural language queries. Despite recent progress with large vision-language models (LVLM…
BiasGuard: A Reasoning-enhanced Bias Detection Tool For Large Language Models
Zhiting Fan, Ruizhe Chen, Zuozhu Liu
Identifying bias in LLM-generated content is a crucial prerequisite for ensuring fairness in LLMs. Existing methods, such as fairness classifiers and LLM-based judges, face limitat…
FairSteer: Inference Time Debiasing for LLMs with Dynamic Activation Steering
Yichen Li, Zhiting Fan, Ruizhe Chen +4
Large language models (LLMs) are prone to capturing biases from training corpus, leading to potential negative social impacts. Existing prompt-based debiasing methods exhibit insta…
FairMT-Bench: Benchmarking Fairness for Multi-turn Dialogue in Conversational LLMs
Zhiting Fan, Ruizhe Chen, Tianxiang Hu +1
The growing use of large language model (LLM)-based chatbots has raised concerns about fairness. Fairness issues in LLMs can lead to severe consequences, such as bias amplification…
BiasAlert: A Plug-and-play Tool for Social Bias Detection in LLMs
Zhiting Fan, Ruizhe Chen, Ruiling Xu +1
Evaluating the bias in Large Language Models (LLMs) becomes increasingly crucial with their rapid development. However, existing evaluation methods rely on fixed-form outputs and c…