Improving Reproducibility in Evaluation through Multi-Level Annotator Modeling
Quick summary
arXiv:2605.13801v2 Announce Type: replace-cross Abstract: As generative AI models such as large language models (LLMs) become more pervasive, ensuring the safety, robustness, and overall trustworthiness of these systems is paramount. However, AI is currently facing a reproducibility crisis driven by unreliable evaluations and unrepeatable experimental results. While human raters are often used to assess models for utility and safety, they introduce divergent biases and subjective opinions into their annotations. Overcoming this variance is exceptionally challenging because very little data exi
Key takeaways
- arXiv:2605.13801v2 Announce Type: replace-cross Abstract: As generative AI models such as large language models (LLMs) become more pervasive, ensuring the safety, robustness, and overall trustworthiness of these systems is paramount.
- However, AI is currently facing a reproducibility crisis driven by unreliable evaluations and unrepeatable experimental results.
- While human raters are often used to assess models for utility and safety, they introduce divergent biases and subjective opinions into their annotations.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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