activity
20212026
most citedOpenAGI: When LLM Meets Domain Experts

76 citations · 293 across the 38 of their papers we have counts for

collaborators

40 papers

cs.AI2026

Understanding and Reducing Misalignment in Persuasion Optimization

Boyi Wei, Ruta Desai, Peter Henderson +1

Frontier models are now trained for complex multi-agent tasks, from running a company to engaging in negotiations. These tasks necessitate training models to be persuasive. But wha…

cs.CL2025

A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation

Zihui Ma, Lingyao Li, Juan Li +4

Rapid, fine-grained disaster damage assessment is essential for effective emergency response, yet remains challenging due to limited ground sensors and delays in official reporting…

cs.LG2025

ADO: Automatic Data Optimization for Inputs in LLM Prompts

Sam Lin, Wenyue Hua, Lingyao Li +2

This study explores a novel approach to enhance the performance of Large Language Models (LLMs) through the optimization of input data within prompts. While previous research has p…

cs.CR2025

Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense

Yang Ouyang, Hengrui Gu, Shuhang Lin +6

As large language models (LLMs) are increasingly deployed in diverse applications, including chatbot assistants and code generation, aligning their behavior with safety and ethical…

cs.CL2025

Toward Equitable Access: Leveraging Crowdsourced Reviews to Investigate Public Perceptions of Health Resource Accessibility

Zhaoqian Xue, Guanhong Liu, Chong Zhang +7

Monitoring health resource disparities during public health crises is critical, yet traditional methods, like surveys, lack the requisite speed and spatial granularity. This study…

cs.AI2024★ 15 cited

Game-theoretic LLM: Agent Workflow for Negotiation Games

Wenyue Hua, Ollie Liu, Lingyao Li +9

This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory. We evaluate several…

cs.CL2024

Disentangling Memory and Reasoning Ability in Large Language Models

Mingyu Jin, Weidi Luo, Sitao Cheng +5

Large Language Models (LLMs) have demonstrated strong performance in handling complex tasks requiring both extensive knowledge and reasoning abilities. However, the existing LLM in…

cs.HC2024★ 1 cited

From Commands to Prompts: LLM-based Semantic File System for AIOS

Zeru Shi, Kai Mei, Mingyu Jin +9

Large language models (LLMs) have demonstrated significant potential in the development of intelligent applications and systems such as LLM-based agents and agent operating systems…

cs.MA2024

Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface

Wenyue Hua, Mengting Wan, Shashank Vadrevu +3

Agents, as user-centric tools, are increasingly deployed for human task delegation, assisting with a broad spectrum of requests by generating thoughts, engaging with user proxies,…

cs.CL2024★ 7 cited

Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education

Huizi Yu, Jiayan Zhou, Lingyao Li +22

Background: Simulated patient systems are important in medical education and research, providing safe, integrative training environments and supporting clinical decision making. Ad…

cs.CL2024

Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models

Qingcheng Zeng, Mingyu Jin, Qinkai Yu +12

Large Language Models (LLMs) are employed across various high-stakes domains, where the reliability of their outputs is crucial. One commonly used method to assess the reliability…

q-fin.TR2024★ 4 cited

When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments

Chong Zhang, Xinyi Liu, Zhongmou Zhang +10

Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fund…

cs.CL2024★ 5 cited

AutoFlow: Automated Workflow Generation for Large Language Model Agents

Zelong Li, Shuyuan Xu, Kai Mei +6

Recent advancements in Large Language Models (LLMs) have shown significant progress in understanding complex natural language. One important application of LLM is LLM-based AI Agen…

cs.CL2024

MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate

Alfonso Amayuelas, Xianjun Yang, Antonis Antoniades +3

Large Language Models (LLMs) have shown exceptional results on current benchmarks when working individually. The advancement in their capabilities, along with a reduction in parame…

cs.CL2024★ 1 cited

Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities

Wenyue Hua, Kaijie Zhu, Lingyao Li +7

This study intends to systematically disentangle pure logic reasoning and text understanding by investigating the contrast across abstract and contextualized logical problems from…

cs.CL2024★ 8 cited

MoralBench: Moral Evaluation of LLMs

Jianchao Ji, Yutong Chen, Mingyu Jin +3

In the rapidly evolving field of artificial intelligence, large language models (LLMs) have emerged as powerful tools for a myriad of applications, from natural language processing…

cs.ET2024★ 21 cited

A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs)

Lingyao Li, Jiayan Zhou, Zhenxiang Gao +8

Electronic Health Records (EHRs) play an important role in the healthcare system. However, their complexity and vast volume pose significant challenges to data interpretation and a…

cs.HC2024

BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis

Shuhang Lin, Wenyue Hua, Lingyao Li +7

This paper presents BattleAgent, an emulation system that combines the Large Vision-Language Model and Multi-agent System. This novel system aims to simulate complex dynamic intera…

cs.CL2024★ 4 cited

Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?

Mingyu Jin, Qinkai Yu, Jingyuan Huang +10

Large language models (LLMs) have shown remarkable performances across a wide range of tasks. However, the mechanisms by which these models encode tasks of varying complexities rem…

cs.IR2024★ 1 cited

IDGenRec: LLM-RecSys Alignment with Textual ID Learning

Juntao Tan, Shuyuan Xu, Wenyue Hua +3

Generative recommendation based on Large Language Models (LLMs) have transformed the traditional ranking-based recommendation style into a text-to-text generation paradigm. However…

cs.DL2024★ 2 cited

Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis

Huizi Yu, Lizhou Fan, Lingyao Li +9

Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), enabling new ways to analyze data, treat patients, and conduct research…

cs.CL2024★ 1 cited

NPHardEval4V: Dynamic Evaluation of Large Vision-Language Models with Effects of Vision

Xiang Li, Wenyue Hua, Kaijie Zhu +8

Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities in multimodal understanding, yet their reasoning abilities remain underexplored. Existing benchmarks…

cs.CL2024★ 2 cited

What if LLMs Have Different World Views: Simulating Alien Civilizations with LLM-based Agents

Zhaoqian Xue, Beichen Wang, Suiyuan Zhu +5

This study introduces "CosmoAgent," an innovative artificial intelligence system that utilizes Large Language Models (LLMs) to simulate complex interactions between human and extra…

cs.CL2024★ 3 cited

EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs

Sam Lin, Wenyue Hua, Zhenting Wang +3

Cloud-based Large Language Models (LLMs) such as ChatGPT have become increasingly integral to daily operations. Nevertheless, they also introduce privacy concerns: firstly, numerou…