A Survey of Large Language Models Attribution
It addresses the problem of enhancing response reliability in conversational AI for researchers, but it is incremental as it reviews existing work rather than proposing new methods.
This paper surveys attribution mechanisms in large language models for open-domain generative systems, highlighting issues like ambiguous knowledge and biases that affect factuality and verifiability, with the goal of providing insights to improve reliability.
Open-domain generative systems have gained significant attention in the field of conversational AI (e.g., generative search engines). This paper presents a comprehensive review of the attribution mechanisms employed by these systems, particularly large language models. Though attribution or citation improve the factuality and verifiability, issues like ambiguous knowledge reservoirs, inherent biases, and the drawbacks of excessive attribution can hinder the effectiveness of these systems. The aim of this survey is to provide valuable insights for researchers, aiding in the refinement of attribution methodologies to enhance the reliability and veracity of responses generated by open-domain generative systems. We believe that this field is still in its early stages; hence, we maintain a repository to keep track of ongoing studies at https://github.com/HITsz-TMG/awesome-llm-attributions.