title: Question Decomposition Improves the Faithfulness of Model-Generated
    Reasoning
summary: As large language models (LLMs) perform more difficult tasks, it becomes
  harder to verify the correctness and safety of their behavior. One approach to
  help with this issue is to prompt LLMs to externalize their reasoning, e.g., by
  having them generate step-by-step reasoning as they answer a question
  (Chain-of-Thought; CoT). The reasoning may enable us to check the process that
  models use to perform tasks. However, this approach relies on the stated
  reasoning faithfully reflecting the model's actual reasoning, which is not
  always the case. To improve over the faithfulness of CoT reasoning, we have
  models generate reasoning by decomposing questions into subquestions.
  Decomposition-based methods achieve strong performance on question-answering
  tasks, sometimes approaching that of CoT while improving the faithfulness of
  the model's stated reasoning on several recently-proposed metrics. By forcing
  the model to answer simpler subquestions in separate contexts, we greatly
  increase the faithfulness of model-generated reasoning over CoT, while still
  achieving some of the performance gains of CoT. Our results show it is possible
  to improve the faithfulness of model-generated reasoning; continued
  improvements may lead to reasoning that enables us to verify the correctness
  and safety of LLM behavior.
paper_authors._id: 64bf3578f671da974e6ac3b2
paper_authors.name: Ansh Radhakrishnan
paper_authors.status: admin_assigned
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paper_authors.fullname: Ansh Radhakrishnan
paper_authors.type: user
paper_authors.user: anshr
paper_authors._id: 64bf3578f671da974e6ac3b3
paper_authors.name: Karina Nguyen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-25T08:33:17.020Z
paper_authors._id: 62ef4469cbe9a53fcd5a7f03
paper_authors.avatarUrl: /avatars/13352152a67ef304ba444cbc310be5dd.svg
paper_authors.fullname: Karina Nguyen
paper_authors.type: user
paper_authors.user: karinanguyen
paper_authors._id: 64bf3578f671da974e6ac3b4
paper_authors.name: Anna Chen
paper_authors._id: 64bf3578f671da974e6ac3b5
paper_authors.name: Carol Chen
paper_authors._id: 64bf3578f671da974e6ac3b6
paper_authors.name: Carson Denison
paper_authors._id: 64bf3578f671da974e6ac3b7
paper_authors.name: Danny Hernandez
paper_authors._id: 64bf3578f671da974e6ac3b8
paper_authors.name: Esin Durmus
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-25T08:35:26.520Z
paper_authors._id: 627dd7dbba19b19eff6aaa46
paper_authors.avatarUrl: /avatars/6e43025ccb96e48339d1db477da043b0.svg
paper_authors.fullname: Esin Durmus
paper_authors.type: user
paper_authors.user: esdurmus
paper_authors._id: 64bf3578f671da974e6ac3b9
paper_authors.name: Evan Hubinger
paper_authors._id: 64bf3578f671da974e6ac3ba
paper_authors.name: Jackson Kernion
paper_authors._id: 64bf3578f671da974e6ac3bb
paper_authors.name: Kamilė Lukošiūtė
paper_authors._id: 64bf3578f671da974e6ac3bc
paper_authors.name: Newton Cheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-25T08:36:13.509Z
paper_authors._id: 6230f73441ca6e8d8f614008
paper_authors.avatarUrl: /avatars/2c6f84c6ecb71c047c5d043d5a89f791.svg
paper_authors.fullname: Newton Cheng
paper_authors.type: user
paper_authors.user: nccheng
paper_authors._id: 64bf3578f671da974e6ac3bd
paper_authors.name: Nicholas Joseph
paper_authors._id: 64bf3578f671da974e6ac3be
paper_authors.name: Nicholas Schiefer
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-25T08:36:39.321Z
paper_authors._id: 63a246e2931d2c13ad842a59
paper_authors.avatarUrl: /avatars/9ac4f3d01c293ce97507be402dfa688c.svg
paper_authors.fullname: Nicholas Schiefer
paper_authors.type: user
paper_authors.user: nschiefer
paper_authors._id: 64bf3578f671da974e6ac3bf
paper_authors.name: Oliver Rausch
paper_authors._id: 64bf3578f671da974e6ac3c0
paper_authors.name: Sam McCandlish
paper_authors._id: 64bf3578f671da974e6ac3c1
paper_authors.name: Sheer El Showk
paper_authors._id: 64bf3578f671da974e6ac3c2
paper_authors.name: Tamera Lanham
paper_authors._id: 64bf3578f671da974e6ac3c3
paper_authors.name: Tim Maxwell
paper_authors._id: 64bf3578f671da974e6ac3c4
paper_authors.name: Venkatesa Chandrasekaran
paper_authors._id: 64bf3578f671da974e6ac3c5
paper_authors.name: Zac Hatfield-Dodds
paper_authors._id: 64bf3578f671da974e6ac3c6
paper_authors.name: Jared Kaplan
paper_authors._id: 64bf3578f671da974e6ac3c7
paper_authors.name: Jan Brauner
paper_authors._id: 64bf3578f671da974e6ac3c8
paper_authors.name: Samuel R. Bowman
paper_authors._id: 64bf3578f671da974e6ac3c9
paper_authors.name: Ethan Perez
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-07-25T02:37:44.958Z
paper_authors._id: 62bb28dc4d3faf9c8938f742
paper_authors.avatarUrl: /avatars/cceeb6b8e679b7460519024cd5e301b5.svg
paper_authors.fullname: Ethan
paper_authors.type: user
paper_authors.user: ethanjperez
paper_title: Question Decomposition Improves the Faithfulness of Model-Generated
    Reasoning
paper_summary: As large language models (LLMs) perform more difficult tasks, it becomes
  harder to verify the correctness and safety of their behavior. One approach to
  help with this issue is to prompt LLMs to externalize their reasoning, e.g., by
  having them generate step-by-step reasoning as they answer a question
  (Chain-of-Thought; CoT). The reasoning may enable us to check the process that
  models use to perform tasks. However, this approach relies on the stated
  reasoning faithfully reflecting the model's actual reasoning, which is not
  always the case. To improve over the faithfulness of CoT reasoning, we have
  models generate reasoning by decomposing questions into subquestions.
  Decomposition-based methods achieve strong performance on question-answering
  tasks, sometimes approaching that of CoT while improving the faithfulness of
  the model's stated reasoning on several recently-proposed metrics. By forcing
  the model to answer simpler subquestions in separate contexts, we greatly
  increase the faithfulness of model-generated reasoning over CoT, while still
  achieving some of the performance gains of CoT. Our results show it is possible
  to improve the faithfulness of model-generated reasoning; continued
  improvements may lead to reasoning that enables us to verify the correctness
  and safety of LLM behavior.
paper_ai_summary: Decomposition-based methods improve the faithfulness of Large Language Model reasoning in question-answering tasks, enhancing the verification of model correctness and safety.
paper_ai_keywords: chain-of-thought decomposition-based methods