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arXiv AI By Youqi Wu, Farzan Farnia

Do Large Language Models Capture the Diversity in their Training Data?

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The paper investigates whether large language models (LLMs) capture the full diversity of outputs present in their training data. Using an information‑theoretic approach, the authors compare the conditional entropy of model‑generated outputs with that of the training data, finding that LLMs consistently produce outputs with lower conditional entropy across various models, scales, and decoding strategies. They also extend the analysis to image and text‑conditioned generators, propose a post‑hoc correction method based on matrix‑entropy projection to increase conditional diversity, and provide theoretical guarantees and an efficient algorithm for this correction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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