Quantifying Generalization Complexity for Large Language Models

摘要

While large language models (LLMs) have shown exceptional capabilities in understanding complex queriesand performing sophisticated tasks, their generalization abilities are often deeply entangled with memorization, necessitating more precise evaluation.To address this challenge, we introduce Scylla, a dynamic evaluation framework that quantitatively measures the generalization abilities of LLMs. Scylla disentangles generalization from memorization via assessing model performance on both in-distribution (ID) and out-of-distribution (OOD) data through 20 tasks across 5 levels of complexity.Through extensive experiments, we uncover a non-monotonic relationship between task complexity and the performance gap between ID and OODdata, which we term the generalization valley.Specifically, this phenomenon reveals a critical threshold---referred toas critical complexity---where reliance on non-generalizable behavior peaks, indicating theupper bound of LLMs' generalization capabilities.As model size increases, the critical complexity shifts toward higher levels of task complexity, suggesting that larger models can handle more complex reasoning tasks before over-relying onmemorization.Leveraging Scylla and the concept of critical complexity, we benchmark 28 LLMs including both open-sourced models such as LLaMA and Qwen families, and closed-sourced models like Claude and GPT, providing a more robust evaluation and establishing a clearer understanding of LLMs' generalization capabilities.