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How Much Do LLMs Hallucinate in Document Q&A Scenarios? A 172-Billion-Token Study Across Temperatures, Context Lengths, and Hardware Platforms [TLDR: 25%] (arxiv.org)
submitted 8 months ago by to c/technology@lemmy.world
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Evaluating 35 open-weight models across three context lengths (32K, 128K, 200K), four temperatures, and three hardware platforms—consuming 172 billion tokens across more than 4,000 runs—we find that the answer is “substantially, and unavoidably.” Even under optimal conditions—best model, best temperature, temperature chosen specifically to minimize fabrication—the floor is non-zero and rises steeply with context length. At 32K, the best model (GLM 4.5) fabricates 1.19% of answers, top-tier models fabricate 5–7%, and the median model fabricates roughly 25%.

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[–] 2 points 8 months ago (2 children)

GLM 4.5 is from August. Isn’t the real tl;dr that a seven month old open model, which was behind proprietary models at the time, did better than most humans would?

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  • [–] 9 points 8 months ago (1 child)

    The task described in this article is asking questions about a document that was provided to the llm in the context.

    I would hope that if you give a human a text and ask them to cite facts from it they would do better than 99% correct.

    Also, when the tokens exceeded 200k, the llm error rate was higher than 10%

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  • [–] 3 points 8 months ago

    I would hope that if you give a human a text and ask them to cite facts from it they would do better than 99% correct.

    That’s literally what school exams are about, isn’t it?

    Token window is a problem for all llms though, that’s not easily solved, but it can be worked around to a certain extent.

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