New Theory Links Memory Compression to Factual Hallucination in LLMs
Researchers have introduced a new information-theoretic framework to explain factual hallucinations in large language models. While previous analyses often attributed errors to missing knowledge, this study highlights a second critical factor: the lossy compression of facts that are actually present in the model's memory. By modeling the interaction between the number of observed facts, the…
Key points
- New rate-distortion bound separates hallucination errors into missing coverage and compression distortion components.
- Finite memory forces approximate storage of observed facts, causing recall errors even when knowledge is present.
- Theory validated via simulations and controlled fact-injection probes in modern language models.
The paper validates these theoretical predictions through simulations and controlled experiments on modern language models. By varying the fact load and effective trainable memory, the researchers demonstrate that even when a model has seen the correct answer, finite memory constraints force approximate storage, leading to recall errors. This work provides a compact way to analyze how selective memory, retrieval mechanisms, and long-context organization impact model reliability. It shifts the focus from purely expanding knowledge bases to optimizing how existing information is encoded and retrieved to minimize distortion.
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