從洞察到行動：可解釋性和分析對自然語言處理的影響研究

摘要

可解釋性和分析（IA）研究是自然語言處理（NLP）領域內一個日益發展的子領域，旨在深入了解NLP系統和方法的行為或內部運作。儘管對該子領域的興趣日益增長，但一個常見的批評是缺乏可操作的見解，因此對NLP的影響有限。在本文中，我們旨在量化IA研究對NLP更廣泛領域的影響。我們通過對以下兩種方法的混合分析來進行：（1）從2018年至2023年在ACL和EMNLP會議上發表的所有論文構建的包含185K+篇論文的引文圖，以及（2）對NLP社區的138名成員進行的調查。我們的定量結果顯示，IA工作在IA之外被廣泛引用，在NLP引文圖中處於核心位置。通過對調查回應的定性分析和對556篇論文的手動標註，我們發現NLP研究人員借鑒了IA工作的研究成果，認為這對NLP的進展、多個子領域至關重要，並依賴其研究成果和術語進行自身工作。許多新穎的方法是基於IA研究結果提出的，並受其極大影響，但高影響力的非IA工作引用了IA研究結果，卻不是由其驅動。最後，我們總結了當前IA工作中的缺失之處，提出號召行動，為IA研究的更有影響力的未來鋪平道路。

English

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.

從洞察到行動：可解釋性和分析對自然語言處理的影響研究

From Insights to Actions: The Impact of Interpretability and Analysis Research on NLP

摘要

Summary

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