Confounds Evaluation and Emergence
Ricardo Dominguez-Olmedo
Florian E. Dorner
Moritz Hardt Max Planck
Abstract
We study a fundamental problem in the evaluation of large language models that we call training on the test task. Unlike wrongful practices like training on the test data, leakage, or data contamination, training on the test task is not a malpractice. Rather, the term describes a growing set of techniques to include task-relevant data in the pretraining stage of a language model. We demonstrate that training on the test task confounds both relative model evaluations and claims about emergent capabilities. We argue that the seeming superiority of one model family over another may be explained by a different degree of training on the test task. To this end, we propose an effective method to adjust for training on the test task by fine-tuning each model under comparison on the same task-relevant data before evaluation. We then show that instances of emergent behavior largely vanish once we adjust for training on the test task. This also applies to reported instances of emergent behavior that cannot be explained by the choice of evaluation metric. Our work promotes a new perspective on the evaluation of large language models with broad implications for benchmarking and the study of emergent capabilities.
This updates me to think that a lot of the emergent behaviors that occured in LLMs probably had mostly mundane reasons, and most importantly this makes me think LLM capabilities might be more predictable than we think.
Another data point supporting Vladimir and Jack Clark’s view of training compute being the key factor:
https://arxiv.org/html/2407.07890v1
Confounds Evaluation and Emergence Ricardo Dominguez-Olmedo Florian E. Dorner Moritz Hardt Max Planck
Abstract
This updates me to think that a lot of the emergent behaviors that occured in LLMs probably had mostly mundane reasons, and most importantly this makes me think LLM capabilities might be more predictable than we think.