Estimating Large Language Model Capabilities without Labeled Test Data (2023)

Abstract: Large Language Models (LLMs) have exhibited an impressive ability to perform in-context learning (ICL) from only a few examples, but the success of ICL varies widely from task to task. Thus, it is important to quickly determine whether ICL is applicable to a new task, but directly evaluating ICL accuracy can be expensive in situations where test data is expensive to annotate—the exact situations where ICL is most appealing. In this paper, we propose the task of ICL accuracy estimation, in which we predict the accuracy of an LLM when doing in-context learning on a new task given only unlabeled test data for that task. To perform ICL accuracy estimation, we propose a method that trains a meta-model using LLM confidence scores as features. We compare our method to several strong accuracy estimation baselines on a new benchmark that covers 4 LLMs and 3 task collections. The meta-model improves over all baselines across 7 out of 12 settings and achieves the same estimation performance as directly evaluating on 40 collected labeled test examples per task. At the same time, no existing approach provides an accurate and reliable ICL accuracy estimation in every setting, highlighting the need for better ways to measure the uncertainty of LLM predictions.

## introduction in-context learning (icl) with large language models (llms) has shown great potential in performing a wide range of language tasks @xcite . icl has the unique advantages of being data-efficient (i.e., only a few labeled training examples are needed) and accessible (i.e., expertise in training models is no longer required). with these advantages, a non-expert user can create a system to perform a new task within minutes by writing a few examples. this gives rise to the popularity of figure 1 : a demonstration of our task setting: given icl accuracy observations of labeled training datasets, we want to estimate the dataset level icl accuracy on the unseen test datasets without labeled test data. we propose the method of training a meta-model based on the confidence score distributions (we denote them as dataset confidence profiles) icl-it is being adopted and tested for a variety of use cases that stretch the boundary of what is considered possible to do with language models. despite the advantages of icl, its performance is highly task-dependent (see figure 6 ). it surpasses expectations on some tasks that are difficult for humans, such as answering trivia questions or riddles, but achieves near-zero performance on seemingly trivial tasks such as some text editing or spelling tasks @xcite . while evalu-ating icl with a labeled test set is a direct solution to know whether icl will be effective, it greatly reduces the appeal of icl, as one of icl's key selling points is that it does not require a large labeled dataset. in addition, many tasks do not come with a labeled test set due to high annotation costs (e.g., medical/law-related questions that require professional knowledge to answer). in such cases, it is highly desirable to estimate the icl performance without a labeled test set. this would help system developers determine whether icl is likely to be useful for their problems of interest. guided by this motivation, we formalize the problem of few-shot icl accuracy estimation: given a handful of labeled in-context examples and a set of unlabeled test examples, our goal is to estimate the overall accuracy of icl on these test examples. our contributions are twofold: our meta-model can estimate icl accuracies without the need for labeled test examples. in 10 out of 12 settings, the meta-model estimates are at least as accurate as directly evaluating on 16 labeled examples. in 2 out of 12 settings, they match with evaluating on 128 labeled examples. on average, we are able to save the annotation cost of 40 test labels per task by using the meta-model. further, the meta-model outperforms all baseline methods in 8 out of 12 settings, improving the relative estimation error by 23.6% however, we also find that there exists substantial room for improvement across all settings. we envision estimating icl accuracy without labeled test data as an open challenge and encourage the community to develop new techniques that can more accurately predict when icl will be effective. ## confidence profile meta-model we propose a new method that trains a meta-model based on the confidence profiles of seen datasets {d i } r @xmath0. we use the term confidence profile to denote the distribution of model confidence scores on each example in the dataset. we extract the confidence profiles (see figure 1 ) from each seen dataset and convert them to a feature vector. we then train a meta-model to map confidence feature vectors to the datasetlevel icl accuracies. the benefits of using the confidence feature vector are twofold. first, we do not need any labeled test data, which saves annotation costs. second, this approach is applicable to any pre-trained language model like gpt3 @xcite and opt @xcite . ## discussion and conclusion in this paper, we study the problem of few-shot icl accuracy estimation. we propose training a meta-model based on llm confidence features and observed accuracies on seen datasets. we show that without using any labeled test data, the meta-model is often able to attain accurate estimates of icl accuracy, which is practically useful for predicting llms' accuracy on datasets that have high annotation costs. we also construct a large-scale benchmark for dataset-level icl accuracy estimation by evaluating the meta-model and multiple baseline methods across 12 evaluation settings and 3 metafeature options. we observe that while some baseline methods can provide good accuracy estimates, our meta-model demonstrates non-trivial improvement in estimation abilities over baseline methods in 8 out of 12 evalutation settings. we encourage future work to develop better meta-model architectures as well as better metafeatures and study potential implications for the meta-model, such as acting as a prompt template/icl example selection method. we believe that our benchmark can serve as an open challenge for improving dataset-level icl accuracy estimations, leading to an improved understanding of when icl is likely to be effective.