5 papers
Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking using Knowledge Graphs
Nan Hu, Jiaoyan Chen, Yike Wu +6
Attributed Question Answering (AQA) has attracted wide attention, but there are still several limitations in evaluating the attributions, including lacking fine-grained attribution…
From Superficial to Deep: Integrating External Knowledge for Follow-up Question Generation Using Knowledge Graph and LLM
Jianyu Liu, Yi Huang, Sheng Bi +2
In a conversational system, dynamically generating follow-up questions based on context can help users explore information and provide a better user experience. Humans are usually…
OneEval: Benchmarking LLM Knowledge-intensive Reasoning over Diverse Knowledge Bases
Yongrui Chen, Zhiqiang Liu, Jing Yu +21
Large Language Models (LLMs) have demonstrated substantial progress on reasoning tasks involving unstructured text, yet their capabilities significantly deteriorate when reasoning…
Magic Mushroom: A Customizable Benchmark for Fine-grained Analysis of Retrieval Noise Erosion in RAG Systems
Yuxin Zhang, Yan Wang, Yongrui Chen +4
Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by incorporating external retrieved information, mitigating issues such as hallucination and outda…
Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models
Jiaqi Li, Qianshan Wei, Chuanyi Zhang +5
Machine unlearning empowers individuals with the `right to be forgotten' by removing their private or sensitive information encoded in machine learning models. However, it remains…