title: Do Large Language Models Latently Perform Multi-Hop Reasoning?
summary: We study whether Large Language Models (LLMs) latently perform multi-hop
  reasoning with complex prompts such as "The mother of the singer of
  'Superstition' is". We look for evidence of a latent reasoning pathway where an
  LLM (1) latently identifies "the singer of 'Superstition'" as Stevie Wonder,
  the bridge entity, and (2) uses its knowledge of Stevie Wonder's mother to
  complete the prompt. We analyze these two hops individually and consider their
  co-occurrence as indicative of latent multi-hop reasoning. For the first hop,
  we test if changing the prompt to indirectly mention the bridge entity instead
  of any other entity increases the LLM's internal recall of the bridge entity.
  For the second hop, we test if increasing this recall causes the LLM to better
  utilize what it knows about the bridge entity. We find strong evidence of
  latent multi-hop reasoning for the prompts of certain relation types, with the
  reasoning pathway used in more than 80% of the prompts. However, the
  utilization is highly contextual, varying across different types of prompts.
  Also, on average, the evidence for the second hop and the full multi-hop
  traversal is rather moderate and only substantial for the first hop. Moreover,
  we find a clear scaling trend with increasing model size for the first hop of
  reasoning but not for the second hop. Our experimental findings suggest
  potential challenges and opportunities for future development and applications
  of LLMs.
paper_authors._id: 65dd6d06f2687e07c2c55638
paper_authors.name: Sohee Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-27T10:45:58.702Z
paper_authors._id: 606ae0adb579bb0e88515311
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1617617060380-noauth.jpeg
paper_authors.fullname: Sohee Yang
paper_authors.type: user
paper_authors.user: soheeyang
paper_authors._id: 65dd6d06f2687e07c2c55639
paper_authors.name: Elena Gribovskaya
paper_authors._id: 65dd6d06f2687e07c2c5563a
paper_authors.name: Nora Kassner
paper_authors._id: 65dd6d06f2687e07c2c5563b
paper_authors.name: Mor Geva
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-27T10:46:29.381Z
paper_authors._id: 610b729f9da682cd54ad9adf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1628140189042-noauth.jpeg
paper_authors.fullname: Mor Geva
paper_authors.type: user
paper_authors.user: mega
paper_authors._id: 65dd6d06f2687e07c2c5563c
paper_authors.name: Sebastian Riedel
paper_title: Do Large Language Models Latently Perform Multi-Hop Reasoning?
paper_summary: We study whether Large Language Models (LLMs) latently perform multi-hop
  reasoning with complex prompts such as "The mother of the singer of
  'Superstition' is". We look for evidence of a latent reasoning pathway where an
  LLM (1) latently identifies "the singer of 'Superstition'" as Stevie Wonder,
  the bridge entity, and (2) uses its knowledge of Stevie Wonder's mother to
  complete the prompt. We analyze these two hops individually and consider their
  co-occurrence as indicative of latent multi-hop reasoning. For the first hop,
  we test if changing the prompt to indirectly mention the bridge entity instead
  of any other entity increases the LLM's internal recall of the bridge entity.
  For the second hop, we test if increasing this recall causes the LLM to better
  utilize what it knows about the bridge entity. We find strong evidence of
  latent multi-hop reasoning for the prompts of certain relation types, with the
  reasoning pathway used in more than 80% of the prompts. However, the
  utilization is highly contextual, varying across different types of prompts.
  Also, on average, the evidence for the second hop and the full multi-hop
  traversal is rather moderate and only substantial for the first hop. Moreover,
  we find a clear scaling trend with increasing model size for the first hop of
  reasoning but not for the second hop. Our experimental findings suggest
  potential challenges and opportunities for future development and applications
  of LLMs.
paper_ai_keywords: Large Language Models (LLMs) multi-hop reasoning bridge entity internal recall reasoning pathway