Do Large Language Models Latently Perform Multi-Hop Reasoning?
Probes whether language models internally compose facts across hops before producing an answer, separating internal computation from verbal explanation.
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Do Large Language Models Latently Perform Multi-Hop Reasoning?
Sohee Yang, Elena Gribovskaya, Nora Kassner, Mor Geva, Sebastian Riedel
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024, pp. 10210-10229.
DOI: 10.18653/v1/2024.acl-long.550
Checked 2026-10-18 against the publication record. The published title, author list or year can differ from the preprint.
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Method comparisons
Recovering information and testing its use
Recovering an intermediate answer from hidden states is different from showing that the model uses it. The multi-hop study by Yang and colleagues separates recall of a bridge entity from its contribution to the final answer, including interventions on internal representations. Evidence was stronger for the first hop than for the complete two-hop computation and varied by relation type. That distinction matters when interpreting claims that a model reasons internally.
Sources: Yang et al. (2024)
Paper details: Do Large Language Models Latently Perform Multi-Hop Reasoning?
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Analysis and evaluation — Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.
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