Revisiting the Uniform Information Density Hypothesis
Clara Meister, Tiago Pimentel, Patrick Haller, Lena A. Jäger, Ryan Cotterell, Roger Levy
Abstract
The uniform information density (UID) hypothesis posits a preference among language users for utterances structured such that information is distributed uniformly across a signal. While its implications on language production have been well explored, the hypothesis potentially makes predictions about language comprehension and linguistic acceptability as well. Further, it is unclear how uniformity in a linguistic signal-or lack thereof-should be measured, and over which linguistic unit, e.g., the sentence or language level, this uniformity should hold. Here we investigate these facets of the UID hypothesis using reading time and acceptability data. While our reading time results are generally consistent with previous work, they are also consistent with a weakly super-linear effect of surprisal, which would be compatible with UID's predictions. For acceptability judgments, we find clearer evidence that non-uniformity in information density is predictive of lower acceptability. We then explore multiple operationalizations of UID, motivated by different interpretations of the original hypothesis, and analyze the scope over which the pressure towards uniformity is exerted. The explanatory power of a subset of the proposed operationalizations suggests that the strongest trend may be a regression towards a mean surprisal across the language, rather than the phrase, sentence, or document-a finding that supports a typical interpretation of UID, namely that it is the byproduct of language users maximizing the use of a (hypothetical) communication channel. 1
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext de9d4f39-3d33-41ec-a21a-489240a1b754Cited by top-tier papers26
- Priors in time: Missing inductive biases for language model interpretabilityEkdeep Singh Lubana, Can Rager, Sai Sumedh R. Hindupur, Valérie Costa et al.ICLR 2026 · 19 citations
- Efficient Transformers with Dynamic Token PoolingPiotr Nawrot, Jan Chorowski, Adrian Lancucki, Edoardo Maria PontiACL 2023 · 14 citations
- Anything Goes? A Crosslinguistic Study of (Im)possible Language Learning in LMsXiulin Yang, Tatsuya Aoyama, Yuekun Yao, Ethan WilcoxACL 2025 · 9 citations
- A Measure-Theoretic Characterization of Tight Language ModelsLi Du, Lucas Torroba Hennigen, Tiago Pimentel, Clara Meister et al.ACL 2023 · 8 citations
- The Harmonic Structure of Information ContoursEleftheria Tsipidi, Samuel Kiegeland, Franz Nowak, Tianyang Xu et al.ACL 2025 · 6 citations
Builds on4
- If beam search is the answer, what was the question?Clara Meister, Ryan Cotterell, Tim VieiraEMNLP 2020 · 26 citations
- A surprisal-duration trade-off across and within the world's languagesTiago Pimentel, Clara Meister, Elizabeth Salesky, Simone Teufel et al.EMNLP 2021 · 2 citations
- Lower Perplexity is Not Always Human-LikeTatsuki Kuribayashi, Yohei Oseki, Takumi Ito, Ryo Yoshida et al.ACL 2021
- A Cognitive Regularizer for Language ModelingJason Wei, Clara Meister, Ryan CotterellACL 2021
Related papers
- Surprise! Uniform Information Density Isn't the Whole Story: Predicting Surprisal Contours in Long-form DiscourseEleftheria Tsipidi, Franz Nowak, Ryan Cotterell, Ethan Wilcox et al.EMNLP 2024 · 2 citations
- Uniform Information Density and Syntactic Reduction: Revisiting that-Mentioning in English Complement ClausesHailin Hao, Elsi KaiserEMNLP 2025
- Expect the Unexpected? Testing the Surprisal of Salient EntitiesJessica Lin, Amir ZeldesACL 2026 · 2 citations
- Information Value: Measuring Utterance Predictability as Distance from Plausible AlternativesMario Giulianelli, Sarenne Wallbridge, Raquel FernándezEMNLP 2023 · 4 citations
- Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in DialogueThomas P. Utting, Mario Giulianelli, Arabella SinclairACL 2026
