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Generating Bilingual Pragmatic Color References.

, , , and . NAACL-HLT, page 2155-2165. Association for Computational Linguistics, (2018)

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Expectations over Unspoken Alternatives Predict Pragmatic Inferences., , , and . CoRR, (2023)A fine-grained comparison of pragmatic language understanding in humans and language models., , , , and . ACL (1), page 4194-4213. Association for Computational Linguistics, (2023)Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches., , , , and . EMNLP (Findings), page 12619-12640. Association for Computational Linguistics, (2023)Pragmatics in Grounded Language Learning: Phenomena, Tasks, and Modeling Approaches., , , , and . CoRR, (2022)A Systematic Assessment of Syntactic Generalization in Neural Language Models., , , , and . ACL, page 1725-1744. Association for Computational Linguistics, (2020)Prompting is not a substitute for probability measurements in large language models., and . EMNLP, page 5040-5060. Association for Computational Linguistics, (2023)Language models align with human judgments on key grammatical constructions., , , , and . CoRR, (2024)Prompt-based methods may underestimate large language models' linguistic generalizations., and . CoRR, (2023)A fine-grained comparison of pragmatic language understanding in humans and language models., , , , and . CoRR, (2022)Generating Bilingual Pragmatic Color References., , , and . NAACL-HLT, page 2155-2165. Association for Computational Linguistics, (2018)