From Insights to Actions: The Impact of Interpretability and Analysis Research on NLP
Marius Mosbach, Vagrant Gautam, Tomás Vergara-Browne, Dietrich Klakow, Mor Geva
2024-06-21
Summary
This paper discusses the importance of interpretability and analysis (IA) research in natural language processing (NLP), aiming to show how this subfield contributes to understanding and improving NLP systems.
What's the problem?
Despite the growing interest in IA research, many people criticize it for not providing practical insights that can be applied to real-world NLP problems. This means that while researchers are studying how NLP models work, their findings often do not lead to significant improvements or changes in the field, limiting their overall impact.
What's the solution?
The authors of the paper conducted a detailed study using two main methods: first, they created a citation graph from over 185,000 research papers published at major NLP conferences to see how often IA research is referenced. Second, they surveyed 138 members of the NLP community to gather opinions on the relevance of IA research. Their findings showed that IA research is frequently cited and is considered important by many researchers, who use its insights to develop new methods and improve existing ones. However, they also noted that influential work outside of IA sometimes references IA findings without fully integrating them into their own research.
Why it matters?
This research is significant because it highlights the value of IA work in advancing NLP. By demonstrating how IA research influences other areas of NLP and identifying gaps in current studies, the authors aim to encourage more practical applications of IA findings. This could lead to better-designed NLP systems that are easier to understand and use, ultimately benefiting researchers and practitioners in the field.
Abstract
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.