Identify useful inductive biases for natural language and reasoning in LLMs

Determine which inductive biases in large language models trained on natural language are useful, particularly for complex reasoning tasks, so that these models exhibit desirable properties beyond low training and test loss.

Background

The paper argues that many desirable behaviors of autoregressive LLMs—such as zero-shot rule extrapolation, in-context learning, and fine-tunability—are not guaranteed by statistical generalization alone in the saturation regime where models achieve near-minimal test loss. Instead, these behaviors often arise due to inductive biases embedded in architectures, training dynamics, or objectives.

Within a discussion of research directions, the authors emphasize that for natural language, especially complex reasoning tasks, the relevant inductive biases remain poorly characterized. They propose focusing on identifying and studying such biases to ensure good out-of-distribution performance and transferability, since current loss-based evaluations do not capture these properties.

References

As opposed to computer vision (Klindt et al., 2021; Geirhos et al., 2018; 2020; 2022; Török et al., 2022; Offert & Bell, 2021; Goyal & Bengio, 2022; Papa et al., 2022), it is unclear what kind of inductive biases are useful for natural languages, especially for more complex tasks such as reasoning.

— Position: Understanding LLMs Requires More Than Statistical Generalization  (2405.01964 - Reizinger et al., 2024) in Section 4, item (iii) Inductive biases

Hybrid approaches that combine Abacus-style inductive biases with large-scale pretraining represent the most promising direction for PG, but integration at scale remains open.

— Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement  (2608.13129 - Ni, 13 Aug 2026) in Section 7, subsection “Future Directions,” paragraph “Algorithmic generalization across arbitrary lengths”