MIT researchers created SymGen, a new tool that makes it easier and faster to validate the responses of large language models (LLMs). This solution solves a prevalent issue in artificial intelligence, when LLMs might provide incorrect or unsubstantiated information, sometimes known as “hallucinations.” This is especially troublesome when AI is utilized in crucial fields such as healthcare or finance, where precision is required. Traditionally, evaluating AI-generated replies has been a laborious and arduous process that required human fact-checkers to sift through extensive papers and data sets.
SymGen addresses this by allowing users to know where the information in an AI-generated response came from. The technology works by directly tying each piece of text generated by the AI to the precise cell in a database or document that the AI references. This openness allows consumers to rapidly determine whether the AI’s response is correct. Users may view the data source by hovering over highlighted areas of the text, allowing them to focus their attention on elements that may require additional verification.
Shannon Shen, a doctoral student at MIT and one of the SymGen inventors, notes that the tool allows users to prioritize what to evaluate, which speeds up the verification process and improves trust in AI replies. In a study conducted by the researchers, SymGen enabled users to assess AI-generated text 20% faster than previous approaches.
The SymGen system works by first providing structured data to the LLM, such as a table of information. The AI generates responses in a symbolic fashion. For example, if the AI mentions a sports team, such as the Portland Trail Blazers, instead of explicitly writing the name, it will refer to the exact cell in the data database that includes “Portland Trail Blazers.” The tool then substitutes the symbolic reference with the real text from the database, ensuring that the response is correct and in line with the data.
According to the researchers, which include co-lead authors Lucas Torroba Hennigen and Aniruddha Nrusimha, this method gives highly thorough and exact references, making it easy to validate the material. It also decreases the possibility of inaccuracies in sections of the text that are directly related to the original data.
While SymGen provides considerable advantages, it still has limits. The system presently only accepts tabular data and relies on the correctness of the source data. If inaccurate data is cited, a human reviewer may miss the error. However, the team is attempting to improve SymGen to handle additional forms of data, such as text, in order to increase its application base. They also intend to test the technology with experts in domains such as healthcare to see if it may help validate AI-generated clinical summaries.
SymGen has the potential to improve the dependability of AI-generated content across a variety of industries by expediting the verification process. The research, which was funded by Liberty Mutual and MIT’s Quest for Intelligence Initiative, was recently presented at the Conference on Language Modeling.
Source: MIT News – Adam Zewe, “Making it easier to verify an AI model’s responses,” published October 21, 2024. You can check out the full article here.
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