Learning Compact Representations of LLM Abilities via Item Response Theory
This work addresses the problem of efficiently utilizing vast LLM resources for practitioners, though it is incremental as it adapts existing IRT methods to a new context.
The paper tackles the challenge of managing large language models (LLMs) by learning compact representations of their abilities using item response theory (IRT), achieving state-of-the-art performance in model routing and benchmark accuracy prediction.
Recent years have witnessed a surge in the number of large language models (LLMs), yet efficiently managing and utilizing these vast resources remains a significant challenge. In this work, we explore how to learn compact representations of LLM abilities that can facilitate downstream tasks, such as model routing and performance prediction on new benchmarks. We frame this problem as estimating the probability that a given model will correctly answer a specific query. Inspired by the item response theory (IRT) in psychometrics, we model this probability as a function of three key factors: (i) the model's multi-skill ability vector, (2) the query's discrimination vector that separates models of differing skills, and (3) the query's difficulty scalar. To learn these parameters jointly, we introduce a Mixture-of-Experts (MoE) network that couples model- and query-level embeddings. Extensive experiments demonstrate that our approach leads to state-of-the-art performance in both model routing and benchmark accuracy prediction. Moreover, analysis validates that the learned parameters encode meaningful, interpretable information about model capabilities and query characteristics.