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LLMs' inability to perform implicit multi-hop reasoning stems from a lack of compositional contexts during pretraining rather than a deficit in individual fact retrieval. The study confirms that exposure to such contexts is a necessary condition for this capability, as data-centric augmentations fail to transfer reasoning skills to individuals absent from these contexts. This finding highlights a fundamental architectural limitation in current models that must be addressed through specific pretraining strategies to achieve robust compositional reasoning.
Multi-Hop Knowledge Composition is Bound by Pretraining Exposure
arXiv:2606.09338v1 Announce Type: new Abstract: Large Language Models fail at implicit multi-hop reasoning: a model answers "When was $X$ born?" and "Who is $Y$'s closest friend?" correctly but fails on "When was $Y$'s closest friend born?" in a single forward pass, even when both facts are perfectly memorized and individually retrievable. We study this failure in a controlled natural language setting with a strict separation between individuals exposed to compositional contexts during pretraining and those that never appear in any such context. We confirm that compositional failure persists even at 97% 1-hop accuracy, establishing the gap as a pretraining failure rather than a knowledge absence. We propose and test nine data-centric augmentation formats and find that compositional pretraining transfers to unseen questions for exposed individuals, but never to individuals absent from compositional pretraining, suggesting that exposure to compositional contexts during pretraining is a necessary condition for implicit multi-hop reasoning.
Abstract:Large Language Models fail at implicit multi-hop reasoning: a model answers "When was $X$ born?" and "Who is $Y$'s closest friend?" correctly but fails on "When was $Y$'s closest friend born?" in a single forward pass, even when both facts are perfectly memorized and individually retrievable. We study this failure in a controlled natural language setting with a strict separation between individuals exposed to compositional contexts during pretraining and those that never appear in any such context. We confirm that compositional failure persists even at 97% 1-hop accuracy, establishing the gap as a pretraining failure rather than a knowledge absence. We propose and test nine data-centric augmentation formats and find that compositional pretraining transfers to unseen questions for exposed individuals, but never to individuals absent from compositional pretraining, suggesting that exposure to compositional contexts during pretraining is a necessary condition for implicit multi-hop reasoning.
Computation and Language (cs.CL)
arXiv:2606.09338 [cs.CL]
(or arXiv:2606.09338v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.09338 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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