title: SCREWS: A Modular Framework for Reasoning with Revisions
summary: 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.
paper_authors._id: 65125545b94d5922e3e92362
paper_authors.name: Kumar Shridhar
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-26T12:54:07.838Z
paper_authors._id: 6336d6df31efcb5647f16ae7
paper_authors.avatarUrl: /avatars/bf168346b825dbf192b0c2bde1581bc5.svg
paper_authors.fullname: Kumar Shridhar
paper_authors.type: user
paper_authors.user: shridhar743
paper_authors._id: 65125545b94d5922e3e92363
paper_authors.name: Harsh Jhamtani
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-26T12:55:01.181Z
paper_authors._id: 650c92c76b94d837c01a7bcb
paper_authors.avatarUrl: /avatars/f618180951083af62522f7fb137d645a.svg
paper_authors.fullname: Jhamtani
paper_authors.type: user
paper_authors.user: randompro19
paper_authors._id: 65125545b94d5922e3e92364
paper_authors.name: Hao Fang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-26T12:55:12.229Z
paper_authors._id: 64d3cba0673c5609e8ecb14d
paper_authors.avatarUrl: /avatars/8f6c52f06f1f77d16e067a0f7f05e1b1.svg
paper_authors.fullname: Hao Fang
paper_authors.type: user
paper_authors.user: haofang-msft
paper_authors._id: 65125545b94d5922e3e92365
paper_authors.name: Benjamin Van Durme
paper_authors.statusLastChangedAt: 2023-09-26T12:55:23.188Z
paper_authors._id: 65125545b94d5922e3e92366
paper_authors.name: Jason Eisner
paper_authors._id: 65125545b94d5922e3e92367
paper_authors.name: Patrick Xia
paper_title: SCREWS: A Modular Framework for Reasoning with Revisions
paper_summary: 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.
paper_ai_keywords: Large language models (LLMs) iteratively refining revising feedback errors roll back homogeneous reasoning method SCREWS modular framework reasoning with revisions Sampling Conditional Resampling Selection unifies previous approaches novel strategies identifying improved reasoning chains state-of-the-art LLMs (ChatGPT and GPT-4) reasoning tasks arithmetic word problems multi-hop question answering code debugging heterogeneous revision strategies selection between original and revised candidates