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Dialogue Games for Benchmarking Language Understanding: Motivation, Taxonomy, Strategy

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arxiv 2304.07007 v1 pith:TQLDOHFT submitted 2023-04-14 cs.CL

classification cs.CL
keywords languageunderstandingtestsdialoguetaxonomyabilitybackgroundformal
verification ladder T0 review T1 audit T2 compute T3 formal

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How does one measure "ability to understand language"? If it is a person's ability that is being measured, this is a question that almost never poses itself in an unqualified manner: Whatever formal test is applied, it takes place on the background of the person's language use in daily social practice, and what is measured is a specialised variety of language understanding (e.g., of a second language; or of written, technical language). Computer programs do not have this background. What does that mean for the applicability of formal tests of language understanding? I argue that such tests need to be complemented with tests of language use embedded in a practice, to arrive at a more comprehensive evaluation of "artificial language understanding". To do such tests systematically, I propose to use "Dialogue Games" -- constructed activities that provide a situational embedding for language use. I describe a taxonomy of Dialogue Game types, linked to a model of underlying capabilites that are tested, and thereby giving an argument for the \emph{construct validity} of the test. I close with showing how the internal structure of the taxonomy suggests an ordering from more specialised to more general situational language understanding, which potentially can provide some strategic guidance for development in this field.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 3 citations worldwide. Full citation record

  1. MDC-R: The Minecraft Dialogue Corpus with Reference

    cs.CL 2025-06 conditional novelty 6.0 of 10

    MDC-R adds expert annotations of anaphoric and deictic reference, with block-level IDs and bounding boxes, to 101 Minecraft building dialogues, and shows that current referring-expression models struggle on this dynam...

  2. Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game

    cs.CL 2025-01 conditional novelty 5.0 of 10

    MaKTO, a pipeline combining expert behavior cloning and KTO preference optimization on multi-agent Werewolf gameplay, reports 61% average win rates against other LLM agents.

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