38 citations · 48 across the 27 of their papers we have counts for
13 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…
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…
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…
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…
How do Humans Process AI-generated Hallucination Contents: a Neuroimaging Study
Shuqi Zhu, Yi Zhong, Ziyi Ye +4
While AI-generated hallucinations pose considerable risks, the underlying cognitive mechanisms by which humans can successfully recognize or be misled by these hallucinations remai…
Parametric Social Identity Injection and Diversification in Public Opinion Simulation
Hexi Wang, Yujia Zhou, Bangde Du +2
Large language models (LLMs) have recently been adopted as synthetic agents for public opinion simulation, offering a promising alternative to costly and slow human surveys. Despit…
ATACompressor: Adaptive Task-Aware Compression for Efficient Long-Context Processing in LLMs
Xuancheng Li, Haitao Li, Yujia Zhou +2
Long-context inputs in large language models (LLMs) often suffer from the "lost in the middle" problem, where critical information becomes diluted or ignored due to excessive lengt…
LegalOne: A Family of Foundation Models for Reliable Legal Reasoning
Haitao Li, Yifan Chen, Shuo Miao +13
While Large Language Models (LLMs) have demonstrated impressive general capabilities, their direct application in the legal domain is often hindered by a lack of precise domain kno…
Beyond Exposure: Optimizing Ranking Fairness with Non-linear Time-Income Functions
Xuancheng Li, Tao Yang, Yujia Zhou +2
Ranking systems in web search and recommendation allocate attention among items and providers, and therefore need to balance relevance-based effectiveness with provider fairness. E…
Beyond Experience Retrieval: Learning to Generate Utility-Optimized Structured Experience for Frozen LLMs
Xuancheng Li, Haitao Li, Yujia Zhou +2
Large language models (LLMs) are largely static and often redo reasoning or repeat mistakes. Prior experience reuse typically relies on external retrieval, which is similarity-base…
MulFeRL: Enhancing Reinforcement Learning with Verbal Feedback in a Multi-turn Loop
Xuancheng Li, Haitao Li, Yujia Zhou +2
Reinforcement Learning with Verifiable Rewards (RLVR) is widely used to improve reasoning across domains, but outcome-only scalar rewards are often sparse and uninformative. This l…