The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing

2026-07-02

AI Summary

This study investigates whether natural language processing (NLP) research is shifting from traditional ACL venues toward general machine learning (ML) conferences and examines the implications of this trend on scholarly publishing dynamics. Leveraging large-scale bibliometric data from 2010 to 2026 and employing cohort tracking alongside causal inference methods, the work provides the first systematic quantification of NLP researchers’ cross-venue submission patterns and their underlying drivers. The findings reveal a 19.2 percentage-point decline in main ACL conference papers, accompanied by a 14.8 percentage-point increase in Findings of ACL and an 8.6 percentage-point rise in ML conference publications. Notably, submissions from new authors to ML venues grew from 5% to 21%, and papers published in ML conferences enjoy a significant citation premium, suggesting that citation incentives are a key motivator behind this migration.

Abstract

Natural Language Processing (NLP) has traditionally been published in its core disciplinary venues like ACL. However, advances in Large Language Models (LLMs) has led to a blurring of the disciplinary lines between NLP and general Machine Learning (ML), with authors regularly publishing in venues from both fields. Here, we ask whether the disciplinary center of gravity is shifting. Using NLP research published from 2010 to 2026 and studies of both established and new authors, we find that a migration is taking place. First, comparing the pre- and post-LLM eras, established authors lost 19.2pp of share at flagship *ACL main-conference tracks while gaining 14.8pp in the newer Findings tracks, and general ML venues rose 8.6pp, even when adjusting for parallel growth in the fields. Second, among newer authors who debut with at least three first-author NLP-topic papers, the share whose work appears mostly at *ACL venues fell from 84% (2019) to 74% (2024), while the share appearing mostly at general ML venues rose from 5% to 21%. Using causal inference techniques, we estimate that these general ML venues confer a significant citation premium, which influences venue selection. Together, these results point to a significant shift in where NLP research is published.

Problem

• Natural Language Processing
• Publication Venues
• Scholarly Migration
• Large Language Models
• Machine Learning

Innovation

• scholarly migration
• large language models
• citation premium
• venue selection
• causal inference