@inproceedings{jindal-etal-2025-bridging,
title = "Bridging the Embodiment Gap in Agricultural Knowledge Representation for Language Models",
author = "Jindal, Vasu and
Ju, Huijin and
Lyu, Zili",
editor = "Zhao, Jin and
Wang, Mingyang and
Liu, Zhu",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-srw.68/",
doi = "10.18653/v1/2025.acl-srw.68",
pages = "929--938",
ISBN = "979-8-89176-254-1",
abstract = "This paper quantifies the ``embodiment gap'' between disembodied language models and embodied agricultural knowledge communication through mixed-methods analysis with 78 farmers. Our key contributions include: (1) the Embodied Knowledge Representation Framework (EKRF), a novel computational architecture with specialized lexical mapping that incorporates embodied linguistic patterns from five identified domains of agricultural expertise; (2) the Embodied Prompt Engineering Protocol (EPEP), which reduced the embodiment gap by 47.3{\%} through systematic linguistic scaffolding techniques; and (3) the Embodied Knowledge Representation Index (EKRI), a new metric for evaluating embodied knowledge representation in language models. Implementation results show substantial improvements across agricultural domains, with particularly strong gains in tool usage discourse (58.7{\%}) and soil assessment terminology (67{\%} reduction in embodiment gap). This research advances both theoretical understanding of embodied cognition in AI and practical methodologies to enhance LLM performance in domains requiring embodied expertise."
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<abstract>This paper quantifies the “embodiment gap” between disembodied language models and embodied agricultural knowledge communication through mixed-methods analysis with 78 farmers. Our key contributions include: (1) the Embodied Knowledge Representation Framework (EKRF), a novel computational architecture with specialized lexical mapping that incorporates embodied linguistic patterns from five identified domains of agricultural expertise; (2) the Embodied Prompt Engineering Protocol (EPEP), which reduced the embodiment gap by 47.3% through systematic linguistic scaffolding techniques; and (3) the Embodied Knowledge Representation Index (EKRI), a new metric for evaluating embodied knowledge representation in language models. Implementation results show substantial improvements across agricultural domains, with particularly strong gains in tool usage discourse (58.7%) and soil assessment terminology (67% reduction in embodiment gap). This research advances both theoretical understanding of embodied cognition in AI and practical methodologies to enhance LLM performance in domains requiring embodied expertise.</abstract>
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%0 Conference Proceedings
%T Bridging the Embodiment Gap in Agricultural Knowledge Representation for Language Models
%A Jindal, Vasu
%A Ju, Huijin
%A Lyu, Zili
%Y Zhao, Jin
%Y Wang, Mingyang
%Y Liu, Zhu
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-254-1
%F jindal-etal-2025-bridging
%X This paper quantifies the “embodiment gap” between disembodied language models and embodied agricultural knowledge communication through mixed-methods analysis with 78 farmers. Our key contributions include: (1) the Embodied Knowledge Representation Framework (EKRF), a novel computational architecture with specialized lexical mapping that incorporates embodied linguistic patterns from five identified domains of agricultural expertise; (2) the Embodied Prompt Engineering Protocol (EPEP), which reduced the embodiment gap by 47.3% through systematic linguistic scaffolding techniques; and (3) the Embodied Knowledge Representation Index (EKRI), a new metric for evaluating embodied knowledge representation in language models. Implementation results show substantial improvements across agricultural domains, with particularly strong gains in tool usage discourse (58.7%) and soil assessment terminology (67% reduction in embodiment gap). This research advances both theoretical understanding of embodied cognition in AI and practical methodologies to enhance LLM performance in domains requiring embodied expertise.
%R 10.18653/v1/2025.acl-srw.68
%U https://aclanthology.org/2025.acl-srw.68/
%U https://doi.org/10.18653/v1/2025.acl-srw.68
%P 929-938
Markdown (Informal)
[Bridging the Embodiment Gap in Agricultural Knowledge Representation for Language Models](https://aclanthology.org/2025.acl-srw.68/) (Jindal et al., ACL 2025)
ACL