Benchmarking Large Language Models in Evaluating Workforce Risk of Robotization: Insights from Agriculture Article Swipe
Lefteris Benos
,
Vasso Marinoudi
,
Patrizia Busato
,
Dimitrios Katerıs
,
Simon Pearson
,
Dionysis Bochtis
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriengineering7040102
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriengineering7040102
Understanding the impact of robotization on the workforce dynamics has become increasingly urgent. While expert assessments provide valuable insights, they are often time-consuming and resource-intensive. Large language models (LLMs) offer a scalable alternative; however, their accuracy and reliability in evaluating workforce robotization potential remain uncertain. This study systematically compares general-purpose LLM-generated assessments with expert evaluations to assess their effectiveness in the agricultural sector by considering human judgments as the ground truth. Using ChatGPT, Copilot, and Gemini, the LLMs followed a three-step evaluation process focusing on (a) task importance, (b) potential for task robotization, and (c) task attribute indexing of 15 agricultural occupations, mirroring the methodology used by human assessors. The findings indicate a significant tendency for LLMs to overestimate robotization potential, with most of the errors falling within the range of 0.229 ± 0.174. This can be attributed primarily to LLM reliance on grey literature and idealized technological scenarios, as well as their limited capacity, to account for the complexities of agricultural work. Future research should focus on integrating expert knowledge into LLM training and improving bias detection and mitigation in agricultural datasets, as well as expanding the range of LLMs studied to enhance assessment reliability.
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- OA Status
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Benchmarking Large Language Models in Evaluating Workforce Risk of Robotization: Insights from AgricultureWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-04-03Full publication date if available
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Lefteris Benos, Vasso Marinoudi, Patrizia Busato, Dimitrios Katerıs, Simon Pearson, Dionysis BochtisList of authors in order
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https://doi.org/10.3390/agriengineering7040102Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/agriengineering7040102Direct OA link when available
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Benchmarking, Workforce, Agriculture, Business, Workforce planning, Knowledge management, Computer science, Economics, Geography, Marketing, Economic growth, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4392453291, https://openalex.org/W4398200901, https://openalex.org/W2949079091, https://openalex.org/W4403045969, https://openalex.org/W4406113248, https://openalex.org/W4392338537, https://openalex.org/W4362610119, https://openalex.org/W4401434014, https://openalex.org/W4377096394, https://openalex.org/W3140384782, https://openalex.org/W6748080884, https://openalex.org/W3157335627, https://openalex.org/W2908531905, https://openalex.org/W2954334197, https://openalex.org/W2987392587, https://openalex.org/W2809179779, https://openalex.org/W3045907619, https://openalex.org/W3208352308, https://openalex.org/W4324258218, https://openalex.org/W3022695253, https://openalex.org/W2526781987, https://openalex.org/W3173434788, https://openalex.org/W2568147334, https://openalex.org/W3122819077, https://openalex.org/W6743808651, https://openalex.org/W3124833732, https://openalex.org/W6758251208, https://openalex.org/W3042413463, https://openalex.org/W4401704028, https://openalex.org/W4404852187, https://openalex.org/W4391679299, https://openalex.org/W4391611012, https://openalex.org/W4386170955, https://openalex.org/W4399848789, https://openalex.org/W4375852308, https://openalex.org/W4230235470, https://openalex.org/W4391930351, https://openalex.org/W2910395230, https://openalex.org/W3136130522, https://openalex.org/W3155140745, https://openalex.org/W4402567763, https://openalex.org/W4406205485, https://openalex.org/W2947727834, https://openalex.org/W4214831823, https://openalex.org/W3088348166, https://openalex.org/W4407264005, https://openalex.org/W4362716218, https://openalex.org/W4285009948, https://openalex.org/W4391560419, https://openalex.org/W2146006411, https://openalex.org/W4406211644, https://openalex.org/W4386142022, https://openalex.org/W4406328129, https://openalex.org/W2988298915, https://openalex.org/W2751744943 |
| referenced_works_count | 55 |
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| abstract_inverted_index.by | 63, 106 |
| abstract_inverted_index.in | 38, 59, 180 |
| abstract_inverted_index.of | 3, 98, 123, 130, 160, 189 |
| abstract_inverted_index.on | 5, 84, 142, 167 |
| abstract_inverted_index.to | 55, 117, 139, 155, 192 |
| abstract_inverted_index.± | 132 |
| abstract_inverted_index.(a) | 85 |
| abstract_inverted_index.(b) | 88 |
| abstract_inverted_index.(c) | 94 |
| abstract_inverted_index.LLM | 140, 172 |
| abstract_inverted_index.The | 109 |
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| abstract_inverted_index.for | 90, 115, 157 |
| abstract_inverted_index.has | 9 |
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| abstract_inverted_index.This | 45, 134 |
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| abstract_inverted_index.their | 34, 57, 152 |
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| abstract_inverted_index.urgent. | 12 |
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| abstract_inverted_index.Copilot, | 73 |
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| cited_by_percentile_year | |
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| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.10986827 |
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| citation_normalized_percentile.is_in_top_10_percent | True |