Question Decomposition Improves the Faithfulness of Model-Generated
  Reasoning
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.
64bf3578f671da974e6ac3b2
Ansh Radhakrishnan
admin_assigned
2023-07-25T08:32:45.650Z
625e0426e1176e6c772aa37c
/avatars/a05c5e3b964ca2a6b82eec0e3d89c064.svg
Ansh Radhakrishnan
user
anshr
64bf3578f671da974e6ac3b3
Karina Nguyen
admin_assigned
2023-07-25T08:33:17.020Z
62ef4469cbe9a53fcd5a7f03
/avatars/13352152a67ef304ba444cbc310be5dd.svg
Karina Nguyen
user
karinanguyen
64bf3578f671da974e6ac3b4
Anna Chen
64bf3578f671da974e6ac3b5
Carol Chen
64bf3578f671da974e6ac3b6
Carson Denison
64bf3578f671da974e6ac3b7
Danny Hernandez
64bf3578f671da974e6ac3b8
Esin Durmus
admin_assigned
2023-07-25T08:35:26.520Z
627dd7dbba19b19eff6aaa46
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Esin Durmus
user
esdurmus
64bf3578f671da974e6ac3b9
Evan Hubinger
64bf3578f671da974e6ac3ba
Jackson Kernion
64bf3578f671da974e6ac3bb
Kamilė Lukošiūtė
64bf3578f671da974e6ac3bc
Newton Cheng
admin_assigned
2023-07-25T08:36:13.509Z
6230f73441ca6e8d8f614008
/avatars/2c6f84c6ecb71c047c5d043d5a89f791.svg
Newton Cheng
user
nccheng
64bf3578f671da974e6ac3bd
Nicholas Joseph
64bf3578f671da974e6ac3be
Nicholas Schiefer
admin_assigned
2023-07-25T08:36:39.321Z
63a246e2931d2c13ad842a59
/avatars/9ac4f3d01c293ce97507be402dfa688c.svg
Nicholas Schiefer
user
nschiefer
64bf3578f671da974e6ac3bf
Oliver Rausch
64bf3578f671da974e6ac3c0
Sam McCandlish
64bf3578f671da974e6ac3c1
Sheer El Showk
64bf3578f671da974e6ac3c2
Tamera Lanham
64bf3578f671da974e6ac3c3
Tim Maxwell
64bf3578f671da974e6ac3c4
Venkatesa Chandrasekaran
64bf3578f671da974e6ac3c5
Zac Hatfield-Dodds
64bf3578f671da974e6ac3c6
Jared Kaplan
64bf3578f671da974e6ac3c7
Jan Brauner
64bf3578f671da974e6ac3c8
Samuel R. Bowman
64bf3578f671da974e6ac3c9
Ethan Perez
extracted_pending
2023-07-25T02:37:44.958Z
62bb28dc4d3faf9c8938f742
/avatars/cceeb6b8e679b7460519024cd5e301b5.svg
Ethan
user
ethanjperez
Question Decomposition Improves the Faithfulness of Model-Generated
  Reasoning
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.
Decomposition-based methods improve the faithfulness of Large Language Model reasoning in question-answering tasks, enhancing the verification of model correctness and safety.
chain-of-thought decomposition-based methods