3 papers
cs.CL2024
SelfPrompt: Autonomously Evaluating LLM Robustness via Domain-Constrained Knowledge Guidelines and Refined Adversarial Prompts
Aihua Pei, Zehua Yang, Shunan Zhu +2
Traditional methods for evaluating the robustness of large language models (LLMs) often rely on standardized benchmarks, which can escalate costs and limit evaluations across varie…
cs.CL2024
KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphs
Aihua Pei, Zehua Yang, Shunan Zhu +3
Existing frameworks for assessing robustness of large language models (LLMs) overly depend on specific benchmarks, increasing costs and failing to evaluate performance of LLMs in p…
cs.AI2024
Reinforcement Learning from Multi-role Debates as Feedback for Bias Mitigation in LLMs
Ruoxi Cheng, Haoxuan Ma, Shuirong Cao +6
Bias in LLMs can harm user experience and societal outcomes. However, current bias mitigation methods often require intensive human feedback, lack transferability to other topics o…