Despite their impressive capabilities, large language models (LLMs) are far from perfect. These artificial intelligence models sometimes “hallucinate” by generating incorrect or unsupported information in response to a query.

Due to this hallucination problem, an LLM’s responses are often verified by human fact-checkers, especially if a model is deployed in a high-stakes setting like healthcare or finance. However, validation processes typically require people to read through long documents cited by the model, a task so onerous and error-prone it could prevent some users from deploying generative AI models in the first place.

To help human validators, MIT researchers created a user-friendly system that enables people to verify an LLM’s responses much more quickly. With this tool, called SymGen, an LLM generates responses with citations that point directly to the place in a source document, such as a given cell in a database.

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