@inproceedings{pham-etal-2025-slm,
title = "{SLM}-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts",
author = "Pham, Nghiem Thanh and
Kieu, Tung and
Nguyen, Duc Manh and
Xuan, Son Ha and
Duong-Trung, Nghia and
Le-Phuoc, Danh",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1165/",
doi = "10.18653/v1/2025.findings-emnlp.1165",
pages = "21369--21392",
ISBN = "979-8-89176-335-7",
abstract = "Small Language Models (SLMs) offer computational efficiency and accessibility, yet a systematic evaluation of their performance and environmental impact remains lacking. We introduce SLM-Bench, the first benchmark specifically designed to assess SLMs across multiple dimensions, including accuracy, computational efficiency, and sustainability metrics. SLM-Bench evaluates 15 SLMs on 9 NLP tasks using 23 datasets spanning 14 domains. The evaluation is conducted on 4 hardware configurations, providing a rigorous comparison of their effectiveness. Unlike prior benchmarks, SLM-Bench quantifies 11 metrics across correctness, computation, and consumption, enabling a holistic assessment of efficiency trade-offs. Our evaluation considers controlled hardware conditions, ensuring fair comparisons across models. We develop an open-source benchmarking pipeline with standardized evaluation protocols to facilitate reproducibility and further research. Our findings highlight the diverse trade-offs among SLMs, where some models excel in accuracy while others achieve superior energy efficiency. SLM-Bench sets a new standard for SLM evaluation, bridging the gap between resource efficiency and real-world applicability."
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%0 Conference Proceedings
%T SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts
%A Pham, Nghiem Thanh
%A Kieu, Tung
%A Nguyen, Duc Manh
%A Xuan, Son Ha
%A Duong-Trung, Nghia
%A Le-Phuoc, Danh
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F pham-etal-2025-slm
%X Small Language Models (SLMs) offer computational efficiency and accessibility, yet a systematic evaluation of their performance and environmental impact remains lacking. We introduce SLM-Bench, the first benchmark specifically designed to assess SLMs across multiple dimensions, including accuracy, computational efficiency, and sustainability metrics. SLM-Bench evaluates 15 SLMs on 9 NLP tasks using 23 datasets spanning 14 domains. The evaluation is conducted on 4 hardware configurations, providing a rigorous comparison of their effectiveness. Unlike prior benchmarks, SLM-Bench quantifies 11 metrics across correctness, computation, and consumption, enabling a holistic assessment of efficiency trade-offs. Our evaluation considers controlled hardware conditions, ensuring fair comparisons across models. We develop an open-source benchmarking pipeline with standardized evaluation protocols to facilitate reproducibility and further research. Our findings highlight the diverse trade-offs among SLMs, where some models excel in accuracy while others achieve superior energy efficiency. SLM-Bench sets a new standard for SLM evaluation, bridging the gap between resource efficiency and real-world applicability.
%R 10.18653/v1/2025.findings-emnlp.1165
%U https://aclanthology.org/2025.findings-emnlp.1165/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1165
%P 21369-21392
Markdown (Informal)
[SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts](https://aclanthology.org/2025.findings-emnlp.1165/) (Pham et al., Findings 2025)
ACL