Summary of Training on the Test Task Confounds Evaluation and Emergence, by Ricardo Dominguez-olmedo et al.
Training on the Test Task Confounds Evaluation and Emergence
by Ricardo Dominguez-Olmedo, Florian E. Dorner, Moritz Hardt
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper investigates the issue of “training on the test task” in evaluating large language models, which is distinct from other problematic practices like data contamination. The authors demonstrate that this phenomenon affects both relative model comparisons and claims about emergent capabilities. They propose a method to adjust for the influence of training on the test task on benchmark evaluations by fine-tuning each model under comparison on the same task-relevant data before evaluation. The study shows that instances of emergent behavior gradually disappear as models train on the test task. This work contributes to a new perspective on evaluating large language models, with implications for benchmarking and understanding emergent capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we evaluate really smart computer programs called language models. We want to make sure they’re actually good at what they do, not just pretending to be. One way we can trick ourselves is by training the model on the same thing we’re using to test it. The authors show that this “training on the test task” makes some models seem better than others when they’re not really. They suggest a simple fix: train each model on the same relevant data before testing them. This helps us get an honest picture of how well language models are doing. |
Keywords
» Artificial intelligence » Fine tuning
