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”

Finally, we explore mechanisms that increase transmission in all three cases. In the first two sets of concepts, where transmission is possible, we find that explicit, direct articulations like explanations, mechanics and descriptions increase transmission the most in optimal messages. In the third case, where transmission appears less feasible, we had hypothesized that, while explicit articulation might fail, tacit knowledge could be transmitted through examples or demonstrations; while we find some evidence of this, it is limited and we are unable to conclude this form of tacit knowledge can increase transmission.

— Articulation Upper Bounds for Language Models  (2610.19721 - Spangher et al., 14 Oct 2026) in Introduction, final paragraph before Section 1