What can large language models do for sustainable food?

Anna Thomas, Adam Yee, Andrew Mayne, Maya B. Mathur, Dan Jurafsky, Kristina Gligorić
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:59377-59433, 2025.

Abstract

Food systems are responsible for a third of human-caused greenhouse gas emissions. We investigate what Large Language Models (LLMs) can contribute to reducing the environmental impacts of food production. We define a typology of design and prediction tasks based on the sustainable food literature and collaboration with domain experts, and evaluate six LLMs on four tasks in our typology. For example, for a sustainable protein design task, food science experts estimated that collaboration with an LLM can reduce time spent by 45% on average, compared to 22% for collaboration with another expert human food scientist. However, for a sustainable menu design task, LLMs produce suboptimal solutions when instructed to consider both human satisfaction and climate impacts. We propose a general framework for integrating LLMs with combinatorial optimization to improve reasoning capabilities. Our approach decreases emissions of food choices by 79% in a hypothetical restaurant while maintaining participants’ satisfaction with their set of choices. Our results demonstrate LLMs’ potential, supported by optimization techniques, to accelerate sustainable food development and adoption.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-thomas25a, title = {What can large language models do for sustainable food?}, author = {Thomas, Anna and Yee, Adam and Mayne, Andrew and Mathur, Maya B. and Jurafsky, Dan and Gligori\'{c}, Kristina}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {59377--59433}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/thomas25a/thomas25a.pdf}, url = {https://proceedings.mlr.press/v267/thomas25a.html}, abstract = {Food systems are responsible for a third of human-caused greenhouse gas emissions. We investigate what Large Language Models (LLMs) can contribute to reducing the environmental impacts of food production. We define a typology of design and prediction tasks based on the sustainable food literature and collaboration with domain experts, and evaluate six LLMs on four tasks in our typology. For example, for a sustainable protein design task, food science experts estimated that collaboration with an LLM can reduce time spent by 45% on average, compared to 22% for collaboration with another expert human food scientist. However, for a sustainable menu design task, LLMs produce suboptimal solutions when instructed to consider both human satisfaction and climate impacts. We propose a general framework for integrating LLMs with combinatorial optimization to improve reasoning capabilities. Our approach decreases emissions of food choices by 79% in a hypothetical restaurant while maintaining participants’ satisfaction with their set of choices. Our results demonstrate LLMs’ potential, supported by optimization techniques, to accelerate sustainable food development and adoption.} }
Endnote
%0 Conference Paper %T What can large language models do for sustainable food? %A Anna Thomas %A Adam Yee %A Andrew Mayne %A Maya B. Mathur %A Dan Jurafsky %A Kristina Gligorić %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-thomas25a %I PMLR %P 59377--59433 %U https://proceedings.mlr.press/v267/thomas25a.html %V 267 %X Food systems are responsible for a third of human-caused greenhouse gas emissions. We investigate what Large Language Models (LLMs) can contribute to reducing the environmental impacts of food production. We define a typology of design and prediction tasks based on the sustainable food literature and collaboration with domain experts, and evaluate six LLMs on four tasks in our typology. For example, for a sustainable protein design task, food science experts estimated that collaboration with an LLM can reduce time spent by 45% on average, compared to 22% for collaboration with another expert human food scientist. However, for a sustainable menu design task, LLMs produce suboptimal solutions when instructed to consider both human satisfaction and climate impacts. We propose a general framework for integrating LLMs with combinatorial optimization to improve reasoning capabilities. Our approach decreases emissions of food choices by 79% in a hypothetical restaurant while maintaining participants’ satisfaction with their set of choices. Our results demonstrate LLMs’ potential, supported by optimization techniques, to accelerate sustainable food development and adoption.
APA
Thomas, A., Yee, A., Mayne, A., Mathur, M.B., Jurafsky, D. & Gligorić, K.. (2025). What can large language models do for sustainable food?. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:59377-59433 Available from https://proceedings.mlr.press/v267/thomas25a.html.

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