mlsanityData to 14 Oct 2026

Information Value: Measuring Utterance Predictability as Distance from Plausible Alternatives

arXiv 2310.13676 ↗First posted 20 Oct 2023cs.CLcited by 23

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Abstract (arXiv)

We present information value, a measure which quantifies the predictability of an utterance relative to a set of plausible alternatives. We introduce a method to obtain interpretable estimates of information value using neural text generators, and exploit their psychometric predictive power to investigate the dimensions of predictability that drive human comprehension behaviour. Information value is a stronger predictor of utterance acceptability in written and spoken dialogue than aggregates of token-level surprisal and it is complementary to surprisal for predicting eye-tracked reading times.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 21 of its 100 references that are in the map. First posted on arXiv 20 Oct 2023.

Builds on

21 of 100 references.

References · 21

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  3. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  4. 2018Neural Network Acceptability Judgmentscited by 958
  5. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  6. 2017DailyDialog: A Manually Labelled Multi-turn Dialogue Datasetcited by 652
  7. 2021Surface Form Competition: Why the Highest Probability Answer Isn't Always Rightcited by 263
  8. 2019Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic Statecited by 138
  9. 2020On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behaviorcited by 119
  10. 2022Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?cited by 113
  11. 2021Revisiting the Uniform Information Density Hypothesiscited by 68
  12. 2021Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translationcited by 65
9 more
  1. 2023What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variabilitycited by 51
  2. 2021Structural Persistence in Language Models: Priming as a Window into Abstract Language Representationscited by 44
  3. 2022Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguitiescited by 30
  4. 2021A surprisal--duration trade-off across and within the world's languagescited by 23
  5. 2020Discourse structure interacts with reference but not syntax in neural language modelscited by 17
  6. 2021A Cognitive Regularizer for Language Modelingcited by 11
  7. 2023Expectations over Unspoken Alternatives Predict Pragmatic Inferencescited by 9
  8. 2022Towards Pragmatic Production Strategies for Natural Language Generation Taskscited by 7
  9. 2023Revisiting Entropy Rate Constancy in Textcited by 7

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Citing papers per year: 2024: 5; 2025: 8; 2026: 10.

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  1. 2025How Linguistics Learned to Stop Worrying and Love the Language Modelscited by 51
  2. 2024M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Modelscited by 27
  3. 2024Generalized Measures of Anticipation and Responsivity in Online Language Processingcited by 24
  4. 2024Surprise! Uniform Information Density Isn't the Whole Story: Predicting Surprisal Contours in Long-form Discoursecited by 17
  5. 2024Towards a Similarity-adjusted Surprisal Theorycited by 9
  6. 2025The Harmonic Structure of Information Contourscited by 9
  7. 2025Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Modelscited by 8
  8. 2026Is Information Density Uniform when Utterances are Grounded on Perception and Discourse?cited by 3
  9. 2024Decomposition of surprisal: Unified computational model of ERP components in language processingcited by 2
  10. 2026On the Proper Treatment of Units in Surprisal Theorycited by 2
  11. 2025Express Your Doubts -- Probabilistic World Modeling Should not be Based on Token logprobscited by 1
  12. 2026Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialoguecited by 1
11 more
  1. 2025Language models as tools for investigating the distinction between possible and impossible natural languagescited by 0
  2. 2025Large language models have learned to use languagecited by 0
  3. 2025Large language models are not about natural languagecited by 0
  4. 2025Linguists should learn to love speech-based deep learning modelscited by 0
  5. 2026Are Language Models Models?cited by 0
  6. 2026DecompressionLM: Deterministic, Diagnostic, and Zero-Shot Concept Graph Extraction from Language Modelscited by 0
  7. 2026Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilitiescited by 0
  8. 2026You Can't Fight in Here! This is BBS!cited by 0
  9. 2026Probing for Reading Timescited by 0
  10. 2026Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appearcited by 0
  11. 2026Not Just an Evaluation: A Case Study of Discourse Particle Sensitivity in Language Modelscited by 0