3 papers
cs.CL2024
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…
cs.IR2024
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…
cs.CL2024
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…