Mohammadtaher Safarzadeh, Hitesh Laxmichand Patel, Afshin Oroojlooy, Graham Horwood, Dan Roth. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

As large language models (LLMs) become increasingly versatile, numerous large scale benchmarks have been developed to thoroughly assess their capabilities. These benchmarks typically consist of diverse datasets and prompts to evaluate different aspects of LLM performance. However, comprehensive evaluations on hundreds or thousands of prompts incur tremendous costs in terms of computation, money, and time. In this work, we investigate strategies to improve evaluation efficiency by selecting a subset of examples from each benchmark using a learned policy. Our approach models the dependencies across test examples, allowing accurate prediction of the evaluation outcomes for the remaining examples based on the outcomes of the selected ones. Consequently, we only need to acquire the actual evaluation outcomes for the selected subset. We rigorously explore various subset selection policies and introduce a novel RL-based policy that leverages the captured dependencies. Empirical results demonstrate that our approach significantly reduces the number of evaluation prompts required while maintaining accurate performance estimates compared to previous methods.

Identifying related entities and events within and across documents is fundamental to natural language understanding.We present an approach to entity and event coreference resolution utilizing contrastive representation learning.Earlier state-of-the-art methods have formulated this problem as a binary classification problem and leveraged large transformers in a cross-encoder architecture to achieve their results.For large collections of documents and corresponding set of n mentions, the necessity of performing n 2 transformer computations in these earlier approaches can be computationally intensive.We show that it is possible to reduce this burden by applying contrastive learning techniques that only require n transformer computations at inference time.Our method achieves state-of-the-art results on a number of key metrics on the ECB+ corpus and is competitive on others.