From Insights to Actions: The Impact of Interpretability and Analysis
  Research on NLP
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.
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Marius Mosbach
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2024-06-21T01:00:53.341Z
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Marius Mosbach
user
mmosbach
6674d0c425c64f92734af3b5
Vagrant Gautam
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2024-06-21T07:06:04.450Z
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Vagrant
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dippedrusk
6674d0c425c64f92734af3b6
Tomás Vergara-Browne
6674d0c425c64f92734af3b7
Dietrich Klakow
6674d0c425c64f92734af3b8
Mor Geva
From Insights to Actions: The Impact of Interpretability and Analysis
  Research on NLP
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.
IA research within NLP is well-cited and influential across multiple subfields, contributing to the development of novel methods and terminology.
Interpretability and analysis NLP citation graph ACL EMNLP survey qualitative analysis