Auxiliary views and generalizable knowledge encoding

Establish whether, during pre-training, large language models benefit from auxiliary views—such as explanations, analogies, and reformulations of the same knowledge—and consequently acquire a more generalizable encoding of that knowledge.

Background

The paper proposes that knowledge in pre-training is represented not only through a primary document but also through auxiliary views, including explanations, analogies, and reformulations generated by other communicative contexts. Its experiments report that allocating a fixed token budget to such views improves both factual recall and inference relative to direct repetition or paraphrasing, with stronger effects for larger models.

The authors elevate this interpretation to a central conjecture: auxiliary views may encourage models to form broader conceptual representations that support more efficient and generalizable knowledge encoding. Although the experiments provide empirical support, the proposition is explicitly presented as a conjecture rather than as an established result.

References

Together, these results lead us to a central conjecture: during pre-training, LLMs benefit from auxiliary views (a web of explanations, analogies, and reformulations that humans generate as they learn and teach each other) and acquire a more generalizable encoding of knowledge.

— Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views  (2609.04180 - Lee et al., 3 Sep 2026) in Introduction, final paragraph before Section 2

However, whether this remains effective for specialized, long-tailed knowledge for which LLMs may lack the expertise to generate high-quality auxiliary views remains an open question.

— Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views  (2609.04180 - Lee et al., 3 Sep 2026) in Section 9, “Discussion & Conclusion,” subsection “Practical Takeaways”