2 citations · 2 across the 10 of their papers we have counts for
13 papers
CLIVO: Differentiable Cluster-Based Instruction Mixing with Learned Validation Objectives
Yuhan Wang, Rui Hu, Batian Di +5
Data selection turns large multimodal instruction pools into compact training subsets tailored to a vision-language model's target capabilities. Effective selection requires both u…
SCBO: Semantically Coherent Batching and Ordering for LLM-Based Social Surveys
Yuanzi Li, Lingjie Wang, Zihang Tian +2
Large Language Models (LLMs) offer a scalable way to simulate survey respondents using demographic profiles and observed reference responses. However, the conventional approach of…
Adaptive Resource Allocation for Effective and Efficient LLM Social Survey Simulation
Yuanzi Li, Xueyang Feng, Junhao Wang +2
Large Language Models (LLMs) enable scalable social survey simulation, yet existing pipelines typically use the same strong general-purpose model and a fixed, often large, responde…
Analyzing and Correcting Benevolence Bias in Large Language Models
Yuanzi Li, Junhao Wang, Minghui Liu +9
Large language models (LLMs) are increasingly used as stand-ins for human respondents, from opinion polls and simulated survey participants to agent-based social simulations. These…
ARCO: Adaptive Rubrics with Co-Evolution for Multi-Step LLM-Based Agents
Zihang Tian, Jingsen Zhang, Rui Li +3
Reinforcement learning for multi-step LLM agents often relies on scalar rewards that indicate success but cannot explain why a trajectory is good or bad. Rubric-based rewards impro…
Towards Fast Domain Adaptation and Fine-Grained User Simulation for Evaluating Conversational Recommender Systems
Yuanzi Li, Quanyu Dai, Xueyang Feng +5
Conversational Recommender Systems (CRSs) enhance user experience through multi-turn interactions, yet evaluating their performance remains challenging. While Large Language Model…
Do Generative Recommenders Deepen the Information Cocoon? A Closed-Loop Simulation with LLM-powered User Simulators
Jiyuan Yang, Gengxin Sun, Mengqi Zhang +5
Recommender systems alleviate information overload, yet repeated feedback between recommendations and user interactions can reinforce existing preferences and narrow users' exposur…
Benchmarking LLMs for Community Governance Simulation with Life-history Narratives
Xu Chen, Yuanzi Li, Lei Wang +6
Effective community governance hinges on understanding what specific residents think and need. Recent work has used large language models (LLMs) to simulate human respondents, offe…
Seeing Before Agreeing: Aligning Multi-Agent Consensus with Visual Evidence
Yuhan Wang, Shuochen Chang, Yalin Feng +8
Vision-language models (VLMs) have achieved strong performance on visual question answering (VQA). To mitigate individual hallucinations and blind spots, aggregating diverse perspe…
Divergence Meets Consensus: A Multi-Source Negative Sampling Framework for Sequential Recommendation
Yuanzi Li, Lingjie Wang, Jingyu Zhao +4
Negative sampling is significant for training sequential recommendation models under implicit feedback. The predominant strategy, self-guided hard negative sampling, selects negati…
LLM Agents as Social Scientists: A Human-AI Collaborative Platform for Social Science Automation
Lei Wang, Yuanzi Li, Jinchao Wu +4
Traditional social science research often requires designing complex experiments across vast methodological spaces and depends on real human participants, making it labor-intensive…
Evaluating and Calibrating LLM Confidence on Questions with Multiple Correct Answers
Yuhan Wang, Shiyu Ni, Zhikai Ding +3
Confidence calibration is essential for making large language models (LLMs) reliable, yet existing training-free methods have been primarily studied under single-answer question an…
Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation
Jiyuan Yang, Yuanzi Li, Jingyu Zhao +8
Sequential Recommenders have been widely applied in various online services, aiming to model users' dynamic interests from their sequential interactions. With users increasingly en…