SCREWS: A Modular Framework for Reasoning with Revisions
Large language models (LLMs) can improve their accuracy on various tasks
through iteratively refining and revising their output based on feedback. We
observe that these revisions can introduce errors, in which case it is better
to roll back to a previous result. Further, revisions are typically
homogeneous: they use the same reasoning method that produced the initial
answer, which may not correct errors. To enable exploration in this space, we
present SCREWS, a modular framework for reasoning with revisions. It is
comprised of three main modules: Sampling, Conditional Resampling, and
Selection, each consisting of sub-modules that can be hand-selected per task.
We show that SCREWS not only unifies several previous approaches under a common
framework, but also reveals several novel strategies for identifying improved
reasoning chains. We evaluate our framework with state-of-the-art LLMs (ChatGPT
and GPT-4) on a diverse set of reasoning tasks and uncover useful new reasoning
strategies for each: arithmetic word problems, multi-hop question answering,
and code debugging. Heterogeneous revision strategies prove to be important, as
does selection between original and revised candidates.
65125545b94d5922e3e92362
Kumar Shridhar
admin_assigned
2023-09-26T12:54:07.838Z
6336d6df31efcb5647f16ae7
/avatars/bf168346b825dbf192b0c2bde1581bc5.svg
Kumar Shridhar
user
shridhar743
65125545b94d5922e3e92363
Harsh Jhamtani
admin_assigned
2023-09-26T12:55:01.181Z
650c92c76b94d837c01a7bcb
/avatars/f618180951083af62522f7fb137d645a.svg
Harsh Jhamtani
user
hjhamtani
65125545b94d5922e3e92364
Hao Fang
admin_assigned
2023-09-26T12:55:12.229Z
64d3cba0673c5609e8ecb14d
/avatars/8f6c52f06f1f77d16e067a0f7f05e1b1.svg
Hao Fang
user
haofang-msft
65125545b94d5922e3e92365
Benjamin Van Durme
2023-09-26T12:55:23.188Z
65125545b94d5922e3e92366
Jason Eisner
65125545b94d5922e3e92367
Patrick Xia
SCREWS: A Modular Framework for Reasoning with Revisions
Large language models (LLMs) can improve their accuracy on various tasks
through iteratively refining and revising their output based on feedback. We
observe that these revisions can introduce errors, in which case it is better
to roll back to a previous result. Further, revisions are typically
homogeneous: they use the same reasoning method that produced the initial
answer, which may not correct errors. To enable exploration in this space, we
present SCREWS, a modular framework for reasoning with revisions. It is
comprised of three main modules: Sampling, Conditional Resampling, and
Selection, each consisting of sub-modules that can be hand-selected per task.
We show that SCREWS not only unifies several previous approaches under a common
framework, but also reveals several novel strategies for identifying improved
reasoning chains. We evaluate our framework with state-of-the-art LLMs (ChatGPT
and GPT-4) on a diverse set of reasoning tasks and uncover useful new reasoning
strategies for each: arithmetic word problems, multi-hop question answering,
and code debugging. Heterogeneous revision strategies prove to be important, as
does selection between original and revised candidates.
A modular framework SCREWS for reasoning with revisions in large language models reveals novel strategies and improves performance across various reasoning tasks by enabling selection and heterogeneous approaches.
SCREWS Sampling Conditional Resampling Selection large language models reasoning chains arithmetic word problems multi-hop question answering code debugging