38 citations · 103 across the 93 of their papers we have counts for
30 papers · 1 filter
Towards Unification of Hallucination Detection and Fact Verification for Large Language Models
Weihang Su, Jianming Long, Changyue Wang +5
Large Language Models (LLMs) frequently exhibit hallucinations, generating content that appears fluent and coherent but is factually incorrect. Such errors undermine trust and hind…
TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation
Bangde Du, Minghao Guo, Songming He +8
Large Language Models (LLMs) are exhibiting emergent human-like abilities and are increasingly envisioned as the foundation for simulating an individual's communication style, beha…
Generalized Pseudo-Relevance Feedback
Yiteng Tu, Weihang Su, Yujia Zhou +4
Query rewriting is a fundamental technique in information retrieval (IR). It typically employs the retrieval result as relevance feedback to refine the query and thereby addresses…
Relative-Based Scaling Law for Neural Language Models
Baoqing Yue, Jinyuan Zhou, Zixi Wei +3
Scaling laws aim to accurately predict model performance across different scales. Existing scaling-law studies almost exclusively rely on cross-entropy as the evaluation metric. Ho…
From <Answer> to <Think>: Multidimensional Supervision of Reasoning Process for LLM Optimization
Beining Wang, Weihang Su, Hongtao Tian +5
Improving the multi-step reasoning ability of Large Language Models (LLMs) is a critical yet challenging task. The dominant paradigm, outcome-supervised reinforcement learning (RLV…
MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems
Qingyao Ai, Yichen Tang, Changyue Wang +3
Scaling up data, parameters, and test-time computation has been the mainstream methods to improve LLM systems (LLMsys), but their upper bounds are almost reached due to the gradual…
What Scales in Cross-Entropy Scaling Law?
Junxi Yan, Zixi Wei, Qingyao Ai +2
The cross-entropy scaling law has long served as a key tool for guiding the development of large language models. It shows that cross-entropy loss decreases in a predictable power-…
JustEva: A Toolkit to Evaluate LLM Fairness in Legal Knowledge Inference
Zongyue Xue, Siyuan Zheng, Shaochun Wang +8
The integration of Large Language Models (LLMs) into legal practice raises pressing concerns about judicial fairness, particularly due to the nature of their "black-box" processes.…
Simulating Dispute Mediation with LLM-Based Agents for Legal Research
Junjie Chen, Haitao Li, Minghao Qin +6
Legal dispute mediation plays a crucial role in resolving civil disputes, yet its empirical study is limited by privacy constraints and complex multivariate interactions. To addres…
Chinese Court Simulation with LLM-Based Agent System
Kaiyuan Zhang, Jiaqi Li, Yueyue Wu +7
Mock trial has long served as an important platform for legal professional training and education. It not only helps students learn about realistic trial procedures, but also provi…
LLMs on Trial: Evaluating Judicial Fairness for Large Language Models
Yiran Hu, Zongyue Xue, Haitao Li +10
Large Language Models (LLMs) are increasingly used in high-stakes fields where their decisions impact rights and equity. However, LLMs' judicial fairness and implications for socia…
SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generation
Weihang Su, Anzhe Xie, Qingyao Ai +5
The rapid growth of academic literature makes the manual creation of scientific surveys increasingly infeasible. While large language models show promise for automating this proces…
Are Recommenders Self-Aware? Label-Free Recommendation Performance Estimation via Model Uncertainty
Jiayu Li, Ziyi Ye, Guohao Jian +4
Can a recommendation model be self-aware? This paper investigates the recommender's self-awareness by quantifying its uncertainty, which provides a label-free estimation of its per…
SelfRACG: Enabling LLMs to Self-Express and Retrieve for Code Generation
Qian Dong, Jia Chen, Qingyao Ai +6
Existing retrieval-augmented code generation (RACG) methods typically use an external retrieval module to fetch semantically similar code snippets used for generating subsequent fr…
Dynamic and Parametric Retrieval-Augmented Generation
Weihang Su, Qingyao Ai, Jingtao Zhan +2
Retrieval-Augmented Generation (RAG) has become a foundational paradigm for equipping large language models (LLMs) with external knowledge, playing a critical role in information r…
ValueSim: Generating Backstories to Model Individual Value Systems
Bangde Du, Ziyi Ye, Zhijing Wu +5
As Large Language Models (LLMs) continue to exhibit increasingly human-like capabilities, aligning them with human values has become critically important. Contemporary advanced tec…
Augmenting Multi-Agent Communication with State Delta Trajectory
Yichen Tang, Weihang Su, Yujia Zhou +4
Multi-agent techniques such as role playing or multi-turn debates have been shown to be effective in improving the performance of large language models (LLMs) in downstream tasks.…
Joint Evaluation of Answer and Reasoning Consistency for Hallucination Detection in Large Reasoning Models
Changyue Wang, Weihang Su, Qingyao Ai +1
Large Reasoning Models (LRMs) extend large language models with explicit, multi-step reasoning traces to enhance transparency and performance on complex tasks. However, these reaso…
Decoupling Reasoning and Knowledge Injection for In-Context Knowledge Editing
Changyue Wang, Weihang Su, Qingyao Ai +2
Knowledge editing aims to efficiently update Large Language Models (LLMs) by modifying specific knowledge without retraining the entire model. Among knowledge editing approaches, i…
Investigating Prosocial Behavior Theory in LLM Agents under Policy-Induced Inequities
Yujia Zhou, Hexi Wang, Qingyao Ai +2
As large language models (LLMs) increasingly operate as autonomous agents in social contexts, evaluating their capacity for prosocial behavior is both theoretically and practically…
JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System
Weihang Su, Baoqing Yue, Qingyao Ai +6
This paper introduces JuDGE (Judgment Document Generation Evaluation), a novel benchmark for evaluating the performance of judgment document generation in the Chinese legal system.…
Enhancing LLM-Based Agents via Global Planning and Hierarchical Execution
Junjie Chen, Haitao Li, Jingli Yang +2
Intelligent agent systems based on Large Language Models (LLMs) have shown great potential in real-world applications. However, existing agent frameworks still face critical limita…
Overview of the NTCIR-18 Automatic Evaluation of LLMs (AEOLLM) Task
Junjie Chen, Haitao Li, Zhumin Chu +2
In this paper, we provide an overview of the NTCIR-18 Automatic Evaluation of LLMs (AEOLLM) task. As large language models (LLMs) grow popular in both academia and industry, how to…
Evaluating Intelligence via Trial and Error
Jingtao Zhan, Jiahao Zhao, Jiayu Li +7
Intelligence is a crucial trait for species to find solutions within a limited number of trial-and-error attempts. Building on this idea, we introduce Survival Game as a framework…