# Do Large Language Models Latently Perform Multi-Hop Reasoning?

Authors: Sohee Yang, Elena Gribovskaya, Nora Kassner, Mor Geva, Sebastian Riedel.

Probes whether language models internally compose facts across hops before producing an answer, separating internal computation from verbal explanation.

First public: 2024-02-26.
ArXiv initial submission: 2024-02-26.

[Original paper](https://arxiv.org/abs/2402.16837) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2402.16837/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2402.16837/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2402.16837/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2402.16837/record.json)

## 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)](https://aclanthology.org/2024.acl-long.550/)

Paper details: [Do Large Language Models Latently Perform Multi-Hop Reasoning?](https://latent-implicit-thinking.github.io/papers/2402.16837/)


## Published version

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.
[Publication](https://aclanthology.org/2024.acl-long.550/) · [BibTeX](https://latent-implicit-thinking.github.io/papers/2402.16837/publication.bib) · [CSL-JSON](https://latent-implicit-thinking.github.io/papers/2402.16837/publication.csl.json)
Metadata checked 2026-10-18 against [the publication record](https://aclanthology.org/2024.acl-long.550/).

## Approaches

- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-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.

Summary and categories are editorial. Read the original paper to verify claims and limitations. A citation to a paper does not, by itself, establish that a later method builds on it.

Reading-list version 0.9.5; literature checked through 2026-10-18.