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Opinion: Reproducibility challenges rise with large language models

The editorial notes that reproducibility has long been a concern in machine learning, and the rapid spread of large language models (LLMs) makes it harder. Since OpenAI’s ChatGPT launched in November 2022, LLMs have been used across many research fields, but their probabilistic nature means the same prompt can yield different text each run. Variations in hardware, software, and floating‑point…

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Key points

  • LLM outputs are probabilistic, so identical prompts can produce different results across runs.
  • Proprietary models hide weights, data, and architecture, hindering exact replication of studies.
  • Authors advise reporting model version, full prompts, hardware/software specs, and hyperparameters.

The authors point out that many studies rely on proprietary models whose weights, training data, and architecture are not publicly disclosed, and providers can update models without notice, making exact replication of older work difficult or impossible. To improve transparency, they recommend reporting the exact model and version, justifying any use of proprietary systems over open‑source alternatives, providing the full prompt text, and logging hardware, software, and hyperparameter settings alongside results. They cite emerging guidelines in the behavioral sciences as a positive sign for broader adoption.

Read the original at Nature Machine Learning primary sourceOpen source ↗
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The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.

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