476 citations · 1.3k across the 134 of their papers we have counts for
137 papers
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…
RWGBench: Evaluating Scholarly Positioning in Related Work Generation
Anzhe Xie, Weihang Su, Jiaxin Mao +4
Large language models have shown strong fluency in scientific writing, yet the evaluation of related work generation (RWG) remains limited. Existing RWG evaluations largely inherit…
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…
What Would You Click? Personalized Video Thumbnail Generation with Preference-aware Highlight Retrieval
Zhiyu He, Zecheng Zhao, Tong Chen +3
Video thumbnails are a key factor for attracting user clicks on video platforms, and are increasingly supported by automation. However, existing thumbnail generation methods typica…
SlimPer: Make Personalization Model Slim and Smart
Siqi Wang, Xianjie Chen, Shaofeng Deng +42
Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely o…
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…
Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift
Weihang Su, Jiacheng Kang, Jingyan Xu +7
Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters can accumulate weight drift, potentially causing cat…
Decoupled Mixture-of-Experts for Parametric Knowledge Injection
Baoqing Yue, Weihang Su, Qingyao Ai +5
Knowledge injection aims to equip large language models (LLMs) with external, domain-specific, or time-sensitive knowledge. Existing approaches typically face a trade-off between f…
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…
Civil Court Simulation with Large Language Models
Yifan Chen, Haitao Li, Kaiyuan Zhang +3
Court simulation bridges legal education and judicial practice, yet human-based simulations are costly and difficult to scale. Large language models (LLMs) offer a scalable alterna…
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…
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…
Enhancing Judgment Document Generation via Agentic Legal Information Collection and Rubric-Guided Optimization
Weihang Su, Xuanyi Chen, Yueyue Wu +2
Automating the drafting of judgment documents is pivotal to judicial efficiency, yet it remains challenging due to the dual requirements of comprehensive retrieval of legal informa…
Decoupling Knowledge and Task Subspaces for Composable Parametric Retrieval Augmented Generation
Weihang Su, Hanwen Zhang, Qingyao Ai +1
Parametric Retrieval-Augmented Generation (PRAG) encodes external documents into lightweight parameter modules that can be retrieved and merged at inference time, offering a promis…
Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation
Dongqi Fu, Kaushik Rangadurai, Haiyu Lu +13
The increase in data volume, computational resources, and model parameters during training has led to the development of numerous large-scale industrial retrieval models for recomm…
TEC: A Collection of Human Trial-and-error Trajectories for Problem Solving
Xinkai Zhang, Jingtao Zhan, Yiqun Liu +1
Trial-and-error is a fundamental strategy for humans to solve complex problems and a necessary capability for Artificial Intelligence (AI) systems operating in real-world environme…
Skill Retrieval Augmentation for Agentic AI
Weihang Su, Jianming Long, Qingyao Ai +6
As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their native parametric capabilities…