Do Large Language Models Latently Perform Multi-Hop Reasoning?
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.
65dd6d06f2687e07c2c55638
Sohee Yang
admin_assigned
2024-02-27T10:45:58.702Z
606ae0adb579bb0e88515311
https://cdn-avatars.huggingface.co/v1/production/uploads/1617617060380-noauth.jpeg
Sohee Yang
user
soheeyang
65dd6d06f2687e07c2c55639
Elena Gribovskaya
65dd6d06f2687e07c2c5563a
Nora Kassner
65dd6d06f2687e07c2c5563b
Mor Geva
admin_assigned
2024-02-27T10:46:29.381Z
610b729f9da682cd54ad9adf
https://cdn-avatars.huggingface.co/v1/production/uploads/1628140189042-noauth.jpeg
Mor Geva
user
mega
65dd6d06f2687e07c2c5563c
Sebastian Riedel
Do Large Language Models Latently Perform Multi-Hop Reasoning?
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.
LLMs exhibit latent multi-hop reasoning for certain complex prompts, with strong evidence of the first reasoning hop and moderate evidence of the second hop, showing scaling trends with model size.
Large Language Models multi-hop reasoning complex prompts latent reasoning pathway bridge entity internal recall contextual utilization scaling trend