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Conjecture: Scaling In-Context Learning to the Pretraining-Corpus Level

Establish whether scaling in-context learning so that models can access and utilize the entire pretraining corpus as context yields stronger and more robust knowledge capabilities than conventional parametric storage.

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Background

Based on observed advantages of in-context learning for knowledge updating, generalization, and conflict handling, the authors conjecture that scaling this mechanism to the full pretraining corpus could surpass limitations of parametric knowledge storage. This conjecture motivates architectures and pipelines that prioritize contextual knowledge utilization.

References

To summarize the viewpoints discussed in this blog post, we propose two conjectures regarding potential improvements to the LLM knowledge paradigm: (1) In-context learning demonstrates certain advantages over traditional LLM knowledge modeling paradigm, and could potentially be scaled up to the pre-training corpus level to enable models to acquire stronger and more robust knowledge capabilities; (2) The hidden states of sequence models may offer a highly generalizable mechanism for knowledge encoding and utilizing, and could potentially serve as a major knowledge storage module.

— Open Problems and a Hypothetical Path Forward in LLM Knowledge Paradigms (2504.06823 - Ye et al., 9 Apr 2025) in Conclusion