100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances
This addresses the need for efficient LLM reliability assessment in high-stakes applications, but it is incremental as it builds on existing assessor methods by leveraging prior evaluations.
The paper tackles the problem of predicting LLM performance on unseen data by reducing the number of evaluations needed, using a generic assessor trained on reference instances from previously tested LLMs; it achieves performance comparable to LLM-specific assessors on in-distribution data, with random selection performing as well as advanced methods, but shows worse performance for out-of-distribution cases.
Predicting the performance of LLMs on individual task instances is essential to ensure their reliability in high-stakes applications. To do so, a possibility is to evaluate the considered LLM on a set of task instances and train an assessor to predict its performance based on features of the instances. However, this approach requires evaluating each new LLM on a sufficiently large set of task instances to train an assessor specific to it. In this work, we leverage the evaluation results of previously tested LLMs to reduce the number of evaluations required to predict the performance of a new LLM. In practice, we propose to test the new LLM on a small set of reference instances and train a generic assessor which predicts the performance of the LLM on an instance based on the performance of the former on the reference set and features of the instance of interest. We conduct empirical studies on HELM-Lite and KindsOfReasoning, a collection of existing reasoning datasets that we introduce, where we evaluate all instruction-fine-tuned OpenAI models until the January 2024 version of GPT4. When predicting performance on instances with the same distribution as those used to train the generic assessor, we find this achieves performance comparable to the LLM-specific assessors trained on the full set of instances. Additionally, we find that randomly selecting the reference instances performs as well as some advanced selection methods we tested. For out of distribution, however, no clear winner emerges and the overall performance is worse, suggesting that the inherent predictability of LLMs is low.