Machine Learning Research Has Outpaced Its Communication Norms and NeurIPS Should Act

Abstract

Machine learning research has grown exponentially while its communication norms have not. We argue NeurIPS should adopt explicit, measurable writing standards. We analyze 2.8 million arXiv papers (1991-2025), 24,772 NeurIPS papers (1987-2024), and 24.5 million PubMed papers (1990-2025), applying classical readability scores, the Hohmann writing style suite (including sensational language), acronym density and reuse, an LLM as judge readability protocol, and citations from OpenAlex and Semantic Scholar. Four patterns emerge. First, NeurIPS abstracts score harder to read on every classical readability metric: Flesch Reading Ease falls from about 24 in 1987 to 13 in 2024, and sensational language rises by about 50 percent in NeurIPS abstracts between 2015 and 2024. Second, acronym density in NeurIPS titles has grown from 0.33 per 100 words in 1987 to 3.21 in 2024, and about 89 percent of NeurIPS acronyms are used fewer than ten times, ten points above the science-wide baseline. Third, more readable NeurIPS papers tend to receive more citations, suggesting readability and impact are correlated and that less readable papers risk remaining fragmented. LLM as judge scores rate NeurIPS abstracts as roughly stable from 1987 to 2022, with early signs of improvement thereafter, a pattern that disagrees with every classical readability metric and raises a design question for enforcement: is the target reader a human or an LLM? Lastly, NeurIPS volume has grown roughly 50-fold between 1987 and 2024. Assuming the goal is to optimise for human readers, we propose seven standards NeurIPS could pilot at NeurIPS 2027: an acronym budget with a venue-approved term list, a human readability threshold, stricter citation standards, standalone visual elements, a plain language summary, a pre-registered acronym glossary, and open source audit tooling.