Open-source LLMs generate less diverse outputs than training data shows
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
Researchers using information-theoretic methods have found that major open-source language models consistently generate outputs with lower entropy than their training data, suggesting they fail to capture the full range of plausible responses present during training. The study, which measures conditional entropy without requiring multiple reference outputs, reveals a systematic gap between model diversity and data diversity across OLMo, Pythia, and GPT-Neo. This finding has implications for practitioners relying on LLMs for tasks requiring genuine output variability, and raises questions about whether current training objectives adequately preserve the distributional properties of source corpora.
Modelwire’s AI-generated summary of coverage from arXiv cs.CL.
Modelwire analysis
ExplainerOur AI-generated reading of the wider context and the next developments to watch.
The study measures a specific gap (conditional entropy loss) without requiring multiple reference outputs, making the finding reproducible across models. But the core claim is narrower than it sounds: models compress diversity, not that they fail to learn from diverse data.
This connects directly to the September 2 finding on logical validity representations. That work showed models can encode sophisticated structure internally while failing to surface it behaviorally. This entropy study suggests a related but distinct problem: models may learn the training distribution but systematically collapse it during generation. The two findings together paint a picture of models that internalize training data richly but then constrain their outputs through decoding choices or learned preferences, raising a harder question about whether low entropy is a bug (poor training) or a feature (learned safety or preference).
If the researchers show that fine-tuning with entropy regularization or alternative decoding methods recovers training data diversity without degrading downstream task performance, that confirms the gap is in generation mechanics rather than learning. If entropy remains low even after such interventions, the problem lies upstream in the training objective itself.
This interpretation is generated from the summary above and the archive coverage cited below. Our methodology · Report an error
Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.CL
Transformers hide logical reasoning in hidden states despite behavioral failure
Researchers probed whether transformer models truly understand logical reasoning or merely pattern-match their way to correct answers. Testing five open-weight models with controlled premise-claim pairs, they found a striking gap: models performed near chance on behavioral tasks, yet their hidden representations encoded logical validity with near-perfect fidelity. This decoding held across unseen templates, domains, and…
MentionsOLMo · Pythia · GPT-Neo
How this coverage is produced
Modelwire uses AI to generate summaries and context from source headlines, snippets, and selected archive coverage. Automated checks do not verify every claim, and items are not routinely reviewed by a person before publication. Zacaria Solis operates the site. Read the linked source for the full evidence and report errors through our corrections process.
Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Do Large Language Models Capture the Diversity in their Training Data?”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.