Is Information Density Uniform when Utterances are Grounded on Perception and Discourse?

• URL: http://arxiv.org/abs/2602.14653v1
• Date: Mon, 16 Feb 2026 11:25:00 GMT
• Title: Is Information Density Uniform when Utterances are Grounded on Perception and Discourse?
• Authors: Matteo Gay, Coleman Haley, Mario Giulianelli, Edoardo Ponti,
• Abstract summary: We present the first computational study of Uniform Information Density (UID) in visually grounded settings.<n>We find that grounding on perception consistently smooths the distribution of information, increasing both global and local uniformity.<n>Overall, this study takes a first step towards modelling the temporal dynamics of information flow in ecologically plausible, multimodal language use.
• Score: 7.3258783042969675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Uniform Information Density (UID) hypothesis posits that speakers are subject to a communicative pressure to distribute information evenly within utterances, minimising surprisal variance. While this hypothesis has been tested empirically, prior studies are limited exclusively to text-only inputs, abstracting away from the perceptual context in which utterances are produced. In this work, we present the first computational study of UID in visually grounded settings. We estimate surprisal using multilingual vision-and-language models over image-caption data in 30 languages and visual storytelling data in 13 languages, together spanning 11 families. We find that grounding on perception consistently smooths the distribution of information, increasing both global and local uniformity across typologically diverse languages compared to text-only settings. In visual narratives, grounding in both image and discourse contexts has additional effects, with the strongest surprisal reductions occurring at the onset of discourse units. Overall, this study takes a first step towards modelling the temporal dynamics of information flow in ecologically plausible, multimodal language use, and finds that grounded language exhibits greater information uniformity, supporting a context-sensitive formulation of UID.

Related papers

• When Meanings Meet: Investigating the Emergence and Quality of Shared Concept Spaces during Multilingual Language Model Training [57.230355403478995]
  We investigate the development of language-agnostic concept spaces during pretraining of EuroLLM.<n>We find that shared concept spaces emerge early and continue to refine, but that alignment with them is language-dependent.<n>In contrast to prior work, our fine-grained manual analysis reveals that some apparent gains in translation quality reflect shifts in behavior.
  arXiv  Detail & Related papers  (2026-01-30T11:23:01Z)
• Beyond the Rosetta Stone: Unification Forces in Generalization Dynamics [56.145578792496714]
  Large language models (LLMs) struggle with cross-lingual knowledge transfer.<n>We study the causes and dynamics of this phenomenon by training small Transformer models from scratch on synthetic multilingual datasets.
  arXiv  Detail & Related papers  (2025-08-14T18:44:13Z)
• Towards Explainable Bilingual Multimodal Misinformation Detection and Localization [64.37162720126194]
  BiMi is a framework that jointly performs region-level localization, cross-modal and cross-lingual consistency detection, and natural language explanation for misinformation analysis.<n>BiMiBench is a benchmark constructed by systematically editing real news images and subtitles.<n>BiMi outperforms strong baselines by up to +8.9 in classification accuracy, +15.9 in localization accuracy, and +2.5 in explanation BERTScore.
  arXiv  Detail & Related papers  (2025-06-28T15:43:06Z)
• Surprise! Uniform Information Density Isn't the Whole Story: Predicting Surprisal Contours in Long-form Discourse [54.08750245737734]
  We propose that speakers modulate information rate based on location within a hierarchically-structured model of discourse. We find that hierarchical predictors are significant predictors of a discourse's information contour and that deeply nested hierarchical predictors are more predictive than shallow ones.
  arXiv  Detail & Related papers  (2024-10-21T14:42:37Z)
• Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models [50.40276881893513]
  This study introduces Spoken Stereoset, a dataset specifically designed to evaluate social biases in Speech Large Language Models (SLLMs) By examining how different models respond to speech from diverse demographic groups, we aim to identify these biases. The findings indicate that while most models show minimal bias, some still exhibit slightly stereotypical or anti-stereotypical tendencies.
  arXiv  Detail & Related papers  (2024-08-14T16:55:06Z)
• Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment [11.148099070407431]
  GroundedBERT is a grounded language learning method that enhances the BERT representation with visually grounded information. Our proposed method significantly outperforms the baseline language models on various language tasks of the GLUE and SQuAD datasets.
  arXiv  Detail & Related papers  (2023-12-04T03:16:48Z)
• Natural Language Decompositions of Implicit Content Enable Better Text Representations [52.992875653864076]
  We introduce a method for the analysis of text that takes implicitly communicated content explicitly into account.<n>We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed.<n>Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP.
  arXiv  Detail & Related papers  (2023-05-23T23:45:20Z)
• Language with Vision: a Study on Grounded Word and Sentence Embeddings [6.231247903840833]
  Grounding language in vision is an active field of research seeking to construct cognitively plausible word and sentence representations. The present study proposes a computational grounding model for pre-trained word embeddings. Our model effectively balances the interplay between language and vision by aligning textual embeddings with visual information.
  arXiv  Detail & Related papers  (2022-06-17T15:04:05Z)
• Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech [26.076534338576234]
  Learning to understand grounded language, which connects natural language to percepts, is a critical research area. In this work we demonstrate the feasibility of performing grounded language acquisition on paired visual percepts and raw speech inputs.
  arXiv  Detail & Related papers  (2021-12-27T16:12:30Z)
• Revisiting the Uniform Information Density Hypothesis [44.277066511088634]
  We investigate the uniform information density (UID) hypothesis using reading time and acceptability data. For acceptability judgments, we find clearer evidence that non-uniformity in information density is predictive of lower acceptability.
  arXiv  Detail & Related papers  (2021-09-23T20:41:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.