202 citations · 286 across the 10 of their papers we have counts for
10 papers
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…
AgentReview: Exploring Peer Review Dynamics with LLM Agents
Yiqiao Jin, Qinlin Zhao, Yiyang Wang +4
Peer review is fundamental to the integrity and advancement of scientific publication. Traditional methods of peer review analyses often rely on exploration and statistics of exist…
Dynamic Evaluation of Large Language Models by Meta Probing Agents
Kaijie Zhu, Jindong Wang, Qinlin Zhao +2
Evaluation of large language models (LLMs) has raised great concerns in the community due to the issue of data contamination. Existing work designed evaluation protocols using well…
The Good, The Bad, and Why: Unveiling Emotions in Generative AI
Cheng Li, Jindong Wang, Yixuan Zhang +7
Emotion significantly impacts our daily behaviors and interactions. While recent generative AI models, such as large language models, have shown impressive performance in various t…
PromptBench: A Unified Library for Evaluation of Large Language Models
Kaijie Zhu, Qinlin Zhao, Hao Chen +2
The evaluation of large language models (LLMs) is crucial to assess their performance and mitigate potential security risks. In this paper, we introduce PromptBench, a unified libr…
CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents
Qinlin Zhao, Jindong Wang, Yixuan Zhang +4
Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning. While most of the work has focused on coope…
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks
Kaijie Zhu, Jiaao Chen, Jindong Wang +3
Large language models (LLMs) have achieved remarkable performance in various evaluation benchmarks. However, concerns are raised about potential data contamination in their conside…
Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning
Kaijie Zhu, Jindong Wang, Xixu Hu +2
Deep neural networks are susceptible to adversarial examples, posing a significant security risk in critical applications. Adversarial Training (AT) is a well-established technique…
A Survey on Evaluation of Large Language Models
Yupeng Chang, Xu Wang, Jindong Wang +13
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to…
PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
Kaijie Zhu, Jindong Wang, Jiaheng Zhou +8
The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts. In response to this…