Surprise! Uniform Information Density Isn’t the Whole Story: Predicting Surprisal Contours in Long-form Discourse (2024.emnlp-main)
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| Challenge: | Uniform Information Density (UID) hypothesis posits that speakers tend to distribute information evenly across linguistic units to achieve efficient communication. |
| Approach: | They propose a functional pressure that speakers modulate information rate based on location within a hierarchically-structured model of discourse. |
| Outcome: | The proposed hypothesis posits that speakers tend to distribute information evenly across linguistic units to achieve efficient communication. |
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| Challenge: | The uniform information density hypothesis posits a preference among language users for utterances structured such that information is distributed uniformly across a signal. |
| Approach: | They propose to test the hypothesis by using reading time and acceptability data to examine the effect of surprisal on language comprehension and acceptabilities. |
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Is Information Density Uniform when Utterances are Grounded on Perception and Discourse? (2026.eacl-long)
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| Challenge: | Existing studies on the distribution of information in visually grounded contexts have focused on text-only inputs. |
| Approach: | They propose to use multilingual vision-and-language models to estimate surprisal . they find grounding on perception increases uniformity across typologically diverse languages . |
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Eleftheria Tsipidi, Samuel Kiegeland, Franz Nowak, Tianyang Xu, Ethan Wilcox, Alex Warstadt, Ryan Cotterell, Mario Giulianelli
| Challenge: | Language typically does not maintain a uniform information rate, but it fluctuates around a global average . a new study suggests periodicity may be a factor in information rate oscillations . |
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| Challenge: | Existing evidence supports the uniform information density hypothesis . however, we re-evaluate the hypothesis with neural language models . |
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Revisiting the Uniform Information Density Hypothesis in LLM Reasoning (2026.findings-acl)
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| Challenge: | a recent study has highlighted the fragility of Chain-of-Thought reasoning . a hypothesis suggests that effective communication is achieved by maintaining a stable flow of information. |
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Expect the Unexpected? Testing the Surprisal of Salient Entities (2026.acl-long)
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| Challenge: | Existing work on the Uniform Information Density hypothesis has neglected the relative salience of discourse participants. |
| Approach: | They propose to use an annotated text to examine how overall salience of entities in discourse relates to surprisal. |
| Outcome: | The proposed method shows that global salience is a mechanism shaping information distribution in discourse. |
A Cognitive Regularizer for Language Modeling (2021.acl-long)
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| Challenge: | a uniform information density hypothesis is used to explain certain linguistic phenomena . a regularizer that encodes the UID hypothesis can be used for language training . |
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A Cross-Linguistic Pressure for Uniform Information Density in Word Order (2023.tacl-1)
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Thomas Hikaru Clark, Clara Meister, Tiago Pimentel, Michael Hahn, Ryan Cotterell, Richard Futrell, Roger Levy
| Challenge: | a recent study has compared real and counterfactual word orders, but one functional pressure has been overlooked . a study of 10 typologically diverse languages shows that real word orders have greater uniformity than reverse word orders . |
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Is Information Density Uniform in Task-Oriented Dialogues? (2021.emnlp-main)
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| Challenge: | Evidence for the uniform information density principle has been found at many levels of language production. |
| Approach: | They propose to use the Uniform Information Density principle to test whether and within which contextual units it holds in task-oriented dialogues. |
| Outcome: | The proposed method is able to reduce fluctuations in the density of the information transmitted. |
How do decoding algorithms distribute information in dialogue responses? (2023.findings-eacl)
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| Challenge: | Using different decoding algorithms, we find that human dialogue generation is beneficial for adherence to the Uniform Information Density principle. |
| Approach: | They investigate whether decoding algorithms implicitly follow the Uniform Information Density principle by distributing information evenly in utterances. |
| Outcome: | The proposed method encourages non-uniform responses, but under low/high surprisal conditions, resulting in poor quality responses. |