Generative AI: Citations for Trust and Consensus

Generative AI uses a large set of sources to create content. The content generated by large language models is text. Often, that text contains statements that are inaccurate or false, sometimes called hallucinations. We explore how identifying citations for the generated text can enable people to determine whether to trust the statements in the text, by allowing different users to specify different trusted sets of sources as candidates for citations. Then we propose methods to eliminate or correct untrustworthy statements. We also consider how citations can help build consensus among people who have different trusted sources of information, by using a large language model to construct text, then editing the text so that it is supported by citations drawn from multiple sets of trusted sources. By using generative AI as a go-between, such a process may allow parties with mutual distrust to discover and confirm areas of agreement. This paper is a proposal for systems that enhance large language models’ usefulness and an outline of some challenges and methods for such systems; it is not a record of system development or testing.