CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity

arXiv:2404.10513v1 Announce Type: cross
Abstract: State-of-the-art performance in QA tasks is currently achieved by systems employing Large Language Models (LLMs), however these models tend to hallucinate information in their responses. One approach focuses on enhancing the generation process by incorporating attribution from the given input to the output. However, the challenge of identifying appropriate attributions and verifying their accuracy against a source is a complex task that requires significant improvements in assessing such systems. We introduce an attribution-oriented Chain-of-Thought reasoning …

abstract art arxiv attribution challenge cs.ai cs.cl cs.lg however information language language models large language large language models llms performance process reasoning responses state systems tasks thought type