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        <title>Language Models Don’t Learn the Physical Manifestation of Language</title>
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            <title>Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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    <abstract>We argue that language-only models don’t learn the physical manifestation of language. We present an empirical investigation of visual-auditory properties of language through a series of tasks, termed H-Test.These tasks highlight a fundamental gap between human linguistic understanding and the sensory-deprived linguistic understanding of LLMs. In support of our hypothesis, 1. deliberate reasoning (Chain-of-Thought), 2. few-shot examples, or 3. stronger LLM from the same model family (LLaMA 2 13B -\textgreater LLaMA 2 70B) has no significant effect on H-Test performance. We bring in the philosophical case of Mary, who learns about the world in a sensory-deprived environment as a useful conceptual framework to understand how language-only models learn about the world (Jackson, 1986). Our experiments show that some of the strongest proprietary LLMs stay near random chance baseline accuracy of 50%, highlighting the limitations of linguistic knowledge acquired in the absence of sensory experience. Our code and data are available at \textlessgithub.com/brucewlee/h-test\textgreater.</abstract>
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    <identifier type="doi">10.18653/v1/2024.acl-long.195</identifier>
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%0 Conference Proceedings
%T Language Models Don’t Learn the Physical Manifestation of Language
%A Lee, Bruce
%A Lim, Jaehyuk
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F lee-lim-2024-language
%X We argue that language-only models don’t learn the physical manifestation of language. We present an empirical investigation of visual-auditory properties of language through a series of tasks, termed H-Test.These tasks highlight a fundamental gap between human linguistic understanding and the sensory-deprived linguistic understanding of LLMs. In support of our hypothesis, 1. deliberate reasoning (Chain-of-Thought), 2. few-shot examples, or 3. stronger LLM from the same model family (LLaMA 2 13B -\textgreater LLaMA 2 70B) has no significant effect on H-Test performance. We bring in the philosophical case of Mary, who learns about the world in a sensory-deprived environment as a useful conceptual framework to understand how language-only models learn about the world (Jackson, 1986). Our experiments show that some of the strongest proprietary LLMs stay near random chance baseline accuracy of 50%, highlighting the limitations of linguistic knowledge acquired in the absence of sensory experience. Our code and data are available at \textlessgithub.com/brucewlee/h-test\textgreater.
%R 10.18653/v1/2024.acl-long.195
%U https://aclanthology.org/2024.acl-long.195
%U https://doi.org/10.18653/v1/2024.acl-long.195
%P 3554-3579

Markdown (Informal)

[Language Models Don’t Learn the Physical Manifestation of Language](https://aclanthology.org/2024.acl-long.195) (Lee & Lim, ACL 2024)

• Language Models Don’t Learn the Physical Manifestation of Language (Lee & Lim, ACL 2024)

ACL

• Bruce Lee and Jaehyuk Lim. 2024. Language Models Don’t Learn the Physical Manifestation of Language. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3554–3579, Bangkok, Thailand. Association for Computational Linguistics.