19 citations · 24 across the 23 of their papers we have counts for
14 papers · 1 filter
EnGRICH: Enhancing Generative Reward Modeling with Critiques from Humans
Xuancheng Li, Beining Wang, Haitao Li +7
Generative reward models (GRMs) are important for LLM optimization. Unlike scalar reward models, GRMs generate natural-language critiques alongside preference judgments, providing…
Composable Parametric Demographic Injection for Values Survey Simulation
Xinyuan Cao, Qingyi Pan, Bangde Du +7
Large language models (LLMs) are increasingly used as simulated respondents in computational social science, yet accurately reproducing population-specific heterogeneous response d…
GenRubric: Self-Evolving Rubric Generation for Scalable LLM Evaluation
Yifan Chen, Haitao Li, Qingyao Ai +4
Large language models are increasingly used as scalable evaluators for open-ended tasks. However, many LLM judges derive query-specific criteria during scoring, leaving the evaluat…
Mitigating Identity Essentialism in LLM Agents with Longitudinal Life Trajectories
Hexi Wang, Yujia Zhou, Bangde Du +7
Large language models (LLMs) offer a scalable approach to social simulation, but their credibility depends on how agents are constructed. Existing methods can partially reproduce p…
Different Feedback, Different Updates: Selective Self-Learning from User Interactions for Large Language Models
Xuancheng Li, Xuanchen Li, Haitao Li +6
User feedback offers natural supervision for persistent LLM improvement, but a single message may support multiple behavioral changes with different scopes of generalization. We in…
Structure-aware Relative Policy Optimization for Ranking
Yiteng Tu, Weihang Su, Zitao Su +3
Ranking is a fundamental component of modern information access systems. Reinforcement learning (RL) provides a flexible framework for directly optimizing coarse-grained feedback a…
MetaSyn: A Benchmark for LLM Agents on Meta-Analysis Articles from Nature Portfolio
Anzhe Xie, Weihang Su, Yujia Zhou +3
Systematic review and meta-analysis is an important method for scientific research. It comprehensively studies target research questions by combining evidence from multiple indepen…
Co-Evolving LLM Evaluators and Policies via DynamicRubric
Beining Wang, Weihang Su, Hongtao Tian +8
Post-training with evaluator feedback on policy-induced samples serves as a major mechanism for improving large language models. As policies improve, these sampled responses become…
Generative Chinese Statute Retrieval
Yiteng Tu, Zitao Su, Weihang Su +5
Statute retrieval is a fundamental task in legal information retrieval, yet existing approaches struggle to bridge the gap between colloquial legal queries and formal statutory lan…
Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations
Xinxin Li, Huiyao Chen, Meishan Zhang +6
Automatic speech recognition (ASR) correction has traditionally focused on isolated utterances or short local contexts. However, as text and speech become increasingly interleaved…
Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models
Changyue Wang, Weihang Su, Qingyao Ai +5
Large language models (LLMs) are widely used to tackle complex tasks with autonomous workflows. Recently, reusable natural language skills have emerged as a popular paradigm to inj…
IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking
Zechen Sun, Yuyang Sun, Zecheng Tang +6
Generating coherent and controllable long-form content remains a persistent challenge for Large Language Models (LLMs). While reasoning-enhanced models have demonstrated success in…
LexRubric: A Rubric-Guided Diagnostic Benchmark for Open-Ended Legal Tasks
Yifan Chen, Haitao Li, Yiran Hu +6
As large language models (LLMs) are increasingly applied to real-world legal tasks, evaluating the reliability of their open-ended legal responses has become essential. These tasks…
Benchmarking LLM-as-a-Judge for Long-Form Output Evaluation
Junjie Chen, Yuxi Dong, Haitao Li +5
As large language models (LLMs) are increasingly used for long-form generation, reliably evaluating long-form outputs has become a critical challenge. LLM-as-a-judge offers a scala…