5 papers
LLMs are Biased Evaluators But Not Biased for Retrieval Augmented Generation
Yen-Shan Chen, Jing Jin, Peng-Ting Kuo +2
Recent studies have demonstrated that large language models (LLMs) exhibit significant biases in evaluation tasks, particularly in preferentially rating and favoring self-generated…
Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models
Cheng-Hsun Hsueh, Paul Kuo-Ming Huang, Tzu-Han Lin +4
Knowledge editing is a rising technique for efficiently updating factual knowledge in large language models (LLMs) with minimal alteration of parameters. However, recent studies ha…
Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization
Yu-Min Tseng, Yu-Chao Huang, Teng-Yun Hsiao +4
The concept of persona, originally adopted in dialogue literature, has re-surged as a promising framework for tailoring large language models (LLMs) to specific context (e.g., pers…
FactAlign: Long-form Factuality Alignment of Large Language Models
Chao-Wei Huang, Yun-Nung Chen
Large language models have demonstrated significant potential as the next-generation information access engines. However, their reliability is hindered by issues of hallucination a…
PairDistill: Pairwise Relevance Distillation for Dense Retrieval
Chao-Wei Huang, Yun-Nung Chen
Effective information retrieval (IR) from vast datasets relies on advanced techniques to extract relevant information in response to queries. Recent advancements in dense retrieval…