aiqbench analysis
This research investigates the causal mechanisms behind knowledge acquisition in large language models during pre-training, specifically highlighting the benefits of auxiliary views and data diversity over simple repetition. The study provides empirical evidence that reformulating information improves factual recall and learning efficiency even when token budgets are constrained, offering new insights into how model compression and layer-wise biases function.
Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views
arXiv:2609.04180v1 Announce Type: cross Abstract: Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during pre-training. We posit that auxiliary views, reformulations of knowledge, are causally helpful for learning. We design controlled experiments to isolate this. First, we confirm that repetition is necessary for acquisition and clarify that paraphrasing helps only at smaller batch sizes. Second, holding the token budget fixed, allocating tokens from document repetition to auxiliary views improves learning, counterintuitively, even for factual recall. Third, the effectiveness of auxiliary views is not contingent on the strength of the teacher model that generates them. Fourth, we identify forms of knowledge, contextual and foundational, that aid learning in the presence of prior knowledge gaps. Finally, we examine how these effects manifest mechanistically via layer-wise biases and compression. Together, our findings suggest that auxiliary representations of knowledge, which arise naturally in large pre-training corpora, are a key factor in the success of pre-training and offer a plausible explanation for why data diversity matters.
Title:Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views
Abstract:Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during pre-training. We posit that auxiliary views, reformulations of knowledge, are causally helpful for learning. We design controlled experiments to isolate this. First, we confirm that repetition is necessary for acquisition and clarify that paraphrasing helps only at smaller batch sizes. Second, holding the token budget fixed, allocating tokens from document repetition to auxiliary views improves learning, counterintuitively, even for factual recall. Third, the effectiveness of auxiliary views is not contingent on the strength of the teacher model that generates them. Fourth, we identify forms of knowledge, contextual and foundational, that aid learning in the presence of prior knowledge gaps. Finally, we examine how these effects manifest mechanistically via layer-wise biases and compression. Together, our findings suggest that auxiliary representations of knowledge, which arise naturally in large pre-training corpora, are a key factor in the success of pre-training and offer a plausible explanation for why data diversity matters.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2609.04180 [cs.CL]
(or arXiv:2609.04180v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.04180 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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