Which one is banana man? Evaluating vision-language models in multi-turn pragmatic interpretation

• URL: http://arxiv.org/abs/2608.29571v1
• Date: Sun, 30 Aug 2026 05:36:41 GMT
• Title: Which one is banana man? Evaluating vision-language models in multi-turn pragmatic interpretation
• Abstract summary: Iterated reference games present a test case for agents' ability to perform context-sensitive pragmatic reasoning.<n>We tested humans and vision--language models on their ability to identify the intended meaning of descriptions produced in iterated reference games.
• Score: 1.0195618602298684
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Flexible adaptation to context and shared pragmatic intuitions contribute to smooth human conversation. Iterated reference games---in which players repeatedly pick out novel referents using language---present a test case for agents' ability to perform context-sensitive pragmatic reasoning in multi-turn linguistic environments. We tested humans and vision--language models on their ability to identify the intended meaning of descriptions produced in iterated reference games, varying the provided context in terms of amount, order, and relevance. While humans performed well consistently, the models we evaluated could make use of prior context to interpret humans' referring expressions, but they struggled to build up the relevant context to interpret those expressions effectively. Our results suggest that the models we evaluated lack core skills needed for efficient linguistic collaboration.

Related papers

• Context informs pragmatic interpretation in vision-language models [1.3941150400549018]
  Iterated reference games present a test case for agents' ability to perform context-sensitive pragmatic reasoning.<n>We tested humans and vision-language models on trials from iterated reference games.
  arXiv  Detail & Related papers  (2025-11-05T23:14:59Z)
• A Psycholinguistic Evaluation of Language Models' Sensitivity to Argument Roles [0.06554326244334868]
  We evaluate large language models' sensitivity to argument roles by replicating psycholinguistic studies on human argument role processing. We find that language models are able to distinguish verbs that appear in plausible and implausible contexts, where plausibility is determined through the relation between the verb and its preceding arguments. This indicates that language models' capacity to detect verb plausibility does not arise from the same mechanism that underlies human real-time sentence processing.
  arXiv  Detail & Related papers  (2024-10-21T16:05:58Z)
• UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations [62.71847873326847]
  We investigate the ability to model unusual, unexpected, and unlikely situations. Given a piece of context with an unexpected outcome, this task requires reasoning abductively to generate an explanation. We release a new English language corpus called UNcommonsense.
  arXiv  Detail & Related papers  (2023-11-14T19:00:55Z)
• A fine-grained comparison of pragmatic language understanding in humans and language models [2.231167375820083]
  We compare language models and humans on seven pragmatic phenomena. We find that the largest models achieve high accuracy and match human error patterns. Preliminary evidence that models and humans are sensitive to similar linguistic cues.
  arXiv  Detail & Related papers  (2022-12-13T18:34:59Z)
• Transparency Helps Reveal When Language Models Learn Meaning [71.96920839263457]
  Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations, both autoregressive and masked language models learn to emulate semantic relations between expressions. Turning to natural language, our experiments with a specific phenomenon -- referential opacity -- add to the growing body of evidence that current language models do not well-represent natural language semantics.
  arXiv  Detail & Related papers  (2022-10-14T02:35:19Z)
• Testing the Ability of Language Models to Interpret Figurative Language [69.59943454934799]
  Figurative and metaphorical language are commonplace in discourse. It remains an open question to what extent modern language models can interpret nonliteral phrases. We introduce Fig-QA, a Winograd-style nonliteral language understanding task.
  arXiv  Detail & Related papers  (2022-04-26T23:42:22Z)
• It's not Rocket Science : Interpreting Figurative Language in Narratives [48.84507467131819]
  We study the interpretation of two non-compositional figurative languages (idioms and similes) Our experiments show that models based solely on pre-trained language models perform substantially worse than humans on these tasks. We additionally propose knowledge-enhanced models, adopting human strategies for interpreting figurative language.
  arXiv  Detail & Related papers  (2021-08-31T21:46:35Z)
• Infusing Finetuning with Semantic Dependencies [62.37697048781823]
  We show that, unlike syntax, semantics is not brought to the surface by today's pretrained models. We then use convolutional graph encoders to explicitly incorporate semantic parses into task-specific finetuning.
  arXiv  Detail & Related papers  (2020-12-10T01:27:24Z)
• Probing Contextual Language Models for Common Ground with Visual Representations [76.05769268286038]
  We design a probing model that evaluates how effective are text-only representations in distinguishing between matching and non-matching visual representations. Our findings show that language representations alone provide a strong signal for retrieving image patches from the correct object categories. Visually grounded language models slightly outperform text-only language models in instance retrieval, but greatly under-perform humans.
  arXiv  Detail & Related papers  (2020-05-01T21:28:28Z)

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.