The Cost of Compression: A Rate-Distortion Limit on Factual Hallucination
Uncovering the mathematical trade-offs between model compression and factual precision in language models.
Factual hallucination in closed-book question answering is often treated as a coverage problem: a model fails because the relevant fact is absent from its internal memory. This view misses a second source of error. Even when a fact has been observed, finite memory may force it to...
ArXiv CS.CL
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This research reveals that factual hallucinations are not just memory gaps, but inherent trade-offs caused by compressing massive datasets into finite parameters. By quantifying the rate-distortion limit, developers can better understand the fundamental constraints preventing perfect information recall in large language models.
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