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LLMs Struggle With Intermediate Task Complexity, But Larger Models Can Handle More Before Over-Reliance on Memorization

Large language models (LLMs) have made impressive advancements in their ability to understand complex queries, generate human-like text, and perform various downstream tasks. However, their generalization capabilities are often entangled with memorization, making it difficult to evaluate their true reasoning abilities. A recent paper introduces a novel evaluation framework called SCYLLA, designed to quantitatively measure the generalization abilities of LLMs.

SCYLLA utilizes a dynamic evaluation framework that disentangles generalization from memorization by assessing model performance on both in-distribution (ID) and out-of-distribution (OOD) data through 20 tasks spanning five levels of complexity. This framework revealed a non-monotonic relationship between task complexity and the performance gap between ID and OOD data. This phenomenon, termed the “generalization valley,” indicates a critical threshold, referred to as “critical complexity,” where LLMs’ reliance on non-generalizable behavior peaks, indicating the upper bound of their generalization capabilities.

The paper further reveals that as model size increases, this critical complexity shifts towards higher levels of task complexity. This suggests that larger models can handle more complex reasoning tasks before over-relying on memorization, indicating their enhanced generalization capacity. These findings challenge the notion that larger models are simply better at regurgitating training data and provide a more robust evaluation of LLMs’ generalization capabilities.

SCYLLA provides valuable insights into the interplay between generalization and memorization in LLMs, helping to establish a clearer understanding of their reasoning abilities. The authors also propose a new metric, the Generalization Score (S), that aims to reward models with strong generalization to OOD data while penalizing those that overfit to ID data. By conducting a comprehensive benchmarking of 28 popular LLMs using SCYLLA, the paper provides a more nuanced understanding of the strengths and weaknesses of current LLMs and their true reasoning capabilities.

These findings have significant implications for the future development of LLMs. The development of more robust evaluation methods and the focus on enhancing generalization capabilities will be crucial for creating LLMs that can truly reason and solve complex problems, going beyond memorization and achieving greater accuracy in challenging scenarios.