title: From Insights to Actions: The Impact of Interpretability and Analysis
    Research on NLP
summary: Interpretability and analysis (IA) research is a growing subfield within NLP
  with the goal of developing a deeper understanding of the behavior or inner
  workings of NLP systems and methods. Despite growing interest in the subfield,
  a commonly voiced criticism is that it lacks actionable insights and therefore
  has little impact on NLP. In this paper, we seek to quantify the impact of IA
  research on the broader field of NLP. We approach this with a mixed-methods
  analysis of: (1) a citation graph of 185K+ papers built from all papers
  published at ACL and EMNLP conferences from 2018 to 2023, and (2) a survey of
  138 members of the NLP community. Our quantitative results show that IA work is
  well-cited outside of IA, and central in the NLP citation graph. Through
  qualitative analysis of survey responses and manual annotation of 556 papers,
  we find that NLP researchers build on findings from IA work and perceive it is
  important for progress in NLP, multiple subfields, and rely on its findings and
  terminology for their own work. Many novel methods are proposed based on IA
  findings and highly influenced by them, but highly influential non-IA work
  cites IA findings without being driven by them. We end by summarizing what is
  missing in IA work today and provide a call to action, to pave the way for a
  more impactful future of IA research.
paper_authors._id: 6674d0c425c64f92734af3b4
paper_authors.name: Marius Mosbach
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-06-21T01:00:53.341Z
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/608865d2511e863acdb20bae/WSJQHZ4PC9hjLMCWv9mSb.png
paper_authors.fullname: Marius Mosbach
paper_authors.type: user
paper_authors.user: mmosbach
paper_authors._id: 6674d0c425c64f92734af3b5
paper_authors.name: Vagrant Gautam
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-21T07:06:04.450Z
paper_authors._id: 645a4c1e21ab438e7328a54b
paper_authors.avatarUrl: /avatars/4fcec7d8e7aa96ea5d52274266e491ad.svg
paper_authors.fullname: Vagrant
paper_authors.type: user
paper_authors.user: dippedrusk
paper_authors._id: 6674d0c425c64f92734af3b6
paper_authors.name: Tomás Vergara-Browne
paper_authors._id: 6674d0c425c64f92734af3b7
paper_authors.name: Dietrich Klakow
paper_authors._id: 6674d0c425c64f92734af3b8
paper_authors.name: Mor Geva
paper_title: From Insights to Actions: The Impact of Interpretability and Analysis
    Research on NLP
paper_summary: Interpretability and analysis (IA) research is a growing subfield within NLP
  with the goal of developing a deeper understanding of the behavior or inner
  workings of NLP systems and methods. Despite growing interest in the subfield,
  a commonly voiced criticism is that it lacks actionable insights and therefore
  has little impact on NLP. In this paper, we seek to quantify the impact of IA
  research on the broader field of NLP. We approach this with a mixed-methods
  analysis of: (1) a citation graph of 185K+ papers built from all papers
  published at ACL and EMNLP conferences from 2018 to 2023, and (2) a survey of
  138 members of the NLP community. Our quantitative results show that IA work is
  well-cited outside of IA, and central in the NLP citation graph. Through
  qualitative analysis of survey responses and manual annotation of 556 papers,
  we find that NLP researchers build on findings from IA work and perceive it is
  important for progress in NLP, multiple subfields, and rely on its findings and
  terminology for their own work. Many novel methods are proposed based on IA
  findings and highly influenced by them, but highly influential non-IA work
  cites IA findings without being driven by them. We end by summarizing what is
  missing in IA work today and provide a call to action, to pave the way for a
  more impactful future of IA research.
paper_ai_keywords: citation graph NLP ACL EMNLP citation analysis survey inter-subfield impact out-of-domain generalization vision qualitative analysis manual annotation survey responses world knowledge terminology progress measurement computational impact actionable insights