Benchmarking the Trade-offs between Performance and Energy Consumption in NLP Model Fine-Tuning

Benchmarking the Trade-offs between Performance and Energy Consumption in NLP Model Fine-Tuning | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 19 October 2025 V1 Latest version Share on Benchmarking the Trade-offs between Performance and Energy Consumption in NLP Model Fine-Tuning Author : Quoc Lap Nguyen 0009-0002-3316-686X [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176086995.51988888/v1 227 views 107 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper presents a detailed empirical study and experience report on software-level optimization techniques aimed at helping practitioners balance model performance with resource consumption during natural language processing model fine-tuning. We conduct a comparative analysis of dynamic batching and selective encoder layer freezing on three Transformer models (BART-base, Flan-T5-Small, and T5-Small), measuring the trade-offs between performance (ROUGE, BERTScore) and resource efficiency (energy, CO2 emissions). Our key finding is that the T5-Small model, when combined with a hybrid optimization approach, offers a near-optimal trade-off: it reduces energy consumption by 80.37% while retaining 99.01% of its baseline ROUGE-1 performance. Across all experiments, these software techniques achieved energy reductions of up to 87.35%. We also report an unexpected finding: dynamic batching, a practical programming technique, can improve model performance, increasing the ROUGE-1 score by up to 3.32% for Flan-T5-Small. Ultimately, this work provides a replicable benchmarking framework and a set of practical guidelines to assist software engineers in designing and implementing more efficient and cost-effective artificial intelligence systems. Supplementary Material File (genqawiley.pdf) Download 143.20 KB Information & Authors Information Version history V1 Version 1 19 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords energy efficiency model compression natural language processing software performance transformer models Authors Affiliations Quoc Lap Nguyen 0009-0002-3316-686X [email protected] University of Economics and Law View all articles by this author Metrics & Citations Metrics Article Usage 227 views 107 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Quoc Lap Nguyen. Benchmarking the Trade-offs between Performance and Energy Consumption in NLP Model Fine-Tuning. Authorea . 19 October 2025. DOI: https://doi.org/10.22541/au.176086995.51988888/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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