Accuracy vs. Reproducibility in Large Language Models
This article explores the issue of inconsistent reasoning paths in LLM outputs, even with the same input, temperature, and sampling configuration. It questions whether we are truly measuring model capability or just the probability of sampling a favorable trajectory.
Why it matters
This highlights a fundamental challenge in evaluating and relying on LLM outputs, as the lack of reproducibility can impact real-world applications and reliability.
Key Points
- 1Identical prompts can produce different reasoning paths in LLM outputs
- 2Current evaluation frameworks assume consistent reasoning process, but this is not always the case
- 3A correct answer does not necessarily imply a stable reasoning process or guarantee reproducibility
- 4Aggregate benchmark scores may hide significant variability in LLM outputs
Details
The article discusses an experiment where the same prompt, model snapshot, temperature, and sampling configuration were used, but the resulting outputs often followed completely different reasoning paths. While the final answers may still be correct, this introduces an important issue - if outputs are path-dependent, then a correct answer does not necessarily imply a stable reasoning process, and passing a benchmark does not guarantee reproducibility. The author questions whether we are truly measuring model capability or just the probability of sampling a favorable trajectory. This may not be a problem of 'better benchmarks,' but rather a question of what benchmarks are actually measuring.
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