6 papers · 1 filter
Beyond Factual Accuracy: Evaluating Global Reasoning Integrity in RAG Systems with LogicScore
Zhichao Yan, Yunxiao Zhao, Jiapu Wang +4
Current evaluation methods for Retrieval Augmented Generation (RAG) suffer from \textit{factual myopia}: they relentlessly emphasize factual accuracy yet neglect global logical int…
Consistency-Aware Editing for Entity-level Unlearning in Language Models
Xiaoqi Han, VÃctor Gutiérrez-Basulto, Ru Li +3
Large language models (LLMs) risk retaining sensitive, copyrighted, or harmful information from their training data. Entity-level unlearning addresses this issue by removing all kn…
Atomic Fact Decomposition Helps Attributed Question Answering
Zhichao Yan, Jiapu Wang, Jiaoyan Chen +3
Attributed Question Answering (AQA) aims to provide both a trustworthy answer and a reliable attribution report for a given question. Retrieval is a widely adopted approach, includ…
Memorization Understanding: Do Large Language Models Have the Ability of Scenario Cognition?
Boxiang Ma, Ru Li, Yuanlong Wang +2
Driven by vast and diverse textual data, large language models (LLMs) have demonstrated impressive performance across numerous natural language processing (NLP) tasks. Yet, a criti…
Explaining Black-box Language Models with Knowledge Probing Systems: A Post-hoc Explanation Perspective
Yunxiao Zhao, Hao Xu, Zhiqiang Wang +3
Pre-trained Language Models (PLMs) are trained on large amounts of unlabeled data, yet they exhibit remarkable reasoning skills. However, the trustworthiness challenges posed by th…
Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities
Zhichao Yan, Jiapu Wang, Jiaoyan Chen +6
Retrieval-Augmented Generation (RAG) shows impressive performance by supplementing and substituting parametric knowledge in Large Language Models (LLMs). Retrieved knowledge can be…