mlsanityData to 7 Oct 2026

A practical introduction to the Rational Speech Act modeling framework

arXiv 2105.09867 ↗First posted 20 May 2021cs.CLcited by 6

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Abstract (arXiv)

Recent advances in computational cognitive science (i.e., simulation-based probabilistic programs) have paved the way for significant progress in formal, implementable models of pragmatics. Rather than describing a pragmatic reasoning process in prose, these models formalize and implement one, deriving both qualitative and quantitative predictions of human behavior -- predictions that consistently prove correct, demonstrating the viability and value of the framework. The current paper provides a practical introduction to and critical assessment of the Bayesian Rational Speech Act modeling framework, unpacking theoretical foundations, exploring technological innovations, and drawing connections to issues beyond current applications.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 12 of its 100 references that are in the map. First posted on arXiv 20 May 2021.

Builds on

12 of 100 references.

References · 12

  1. 2018Speaker-Follower Models for Vision-and-Language Navigationcited by 337
  2. 2016Reasoning About Pragmatics with Neural Listeners and Speakerscited by 68
  3. 2017Colors in Context: A Pragmatic Neural Model for Grounded Language Understandingcited by 57
  4. 2018Pragmatically Informative Image Captioning with Character-Level Inferencecited by 42
  5. 2019Characterizing the dynamics of learning in repeated reference gamescited by 34
  6. 2019Pragmatically Informative Text Generationcited by 32
  7. 2019When redundancy is useful: A Bayesian approach to 'overinformative' referring expressionscited by 21
  8. 2020A Rate-Distortion view of human pragmatic reasoningcited by 19
  9. 2019Towards a Quantum-Like Cognitive Architecture for Decision-Makingcited by 17
  10. 2015Learning in the Rational Speech Acts Modelcited by 16
  11. 2018An Incremental Iterated Response Model of Pragmaticscited by 11
  12. 2019Lost in Machine Translation: A Method to Reduce Meaning Losscited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 1.

6 papers.

  1. 2024Bayesian Statistical Modeling with Predictors from LLMscited by 6
  2. 2025Lumos: Let there be Language Model System Certificationcited by 1
  3. 2025Direct Preference Optimization for Adaptive Concept-based Explanationscited by 0
  4. 2025Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causalitycited by 0
  5. 2025Planning Ahead with RSA: Efficient Signalling in Dynamic Environments by Projecting User Awareness across Future Timestepscited by 0
  6. 2026Compositionality and the lexicon in evolutionary semanticscited by 0