How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning

Abstract: Artificial intelligence systems significantly impact the environment, particularly in NLP tasks. These tasks often require extensive computational resources to train deep neural networks, including large-scale LLMs containing billions of parameters. This study analyzes the trade-offs between energy consumption and performance across three neural LLMs: two pre-trained models (T5-base and BART-base), and one LLM (LLaMA-3-8B). These models were fine-tuned for the text summarization task, focusing on generating research paper highlights that encapsulate the core themes of each paper. The carbon footprint associated with fine-tuning each model was measured, offering a comprehensive assessment of their environmental impact. It is observed that LLaMA-3-8B produces the largest carbon footprint among the three models. A wide range of evaluation metrics, including ROUGE, METEOR, MoverScore, BERTScore, and SciBERTScore, were employed to assess the performance of the models on the given task. This research underscores the importance of incorporating environmental considerations into the design and implementation of neural LLMs and calls for the advancement of energy-efficient AI methodologies.

• The paper analyzes the energy consumption and carbon footprint of fine-tuning T5-base, BART-base, and LLaMA 3-8B neural language models for text summarization.
• Key findings reveal a trade-off where larger models like LLaMA 3-8B demonstrate higher energy consumption and carbon footprint, despite competitive or improved performance on certain metrics.
• The study underscores the need for balancing AI performance with environmental sustainability, advocating for energy efficiency to be integrated as a primary design criterion in future model development.

The paper "How Green are Neural LLMs? Analyzing Energy Consumption in Text Summarization Fine-tuning" focuses on the environmental impact of fine-tuning neural LLMs for text summarization tasks. This research addresses the growing concern about the substantial energy consumption and subsequent carbon footprint associated with deep neural networks, particularly in the field of NLP. The authors investigate three models: T5-base, BART-base, and LLaMA 3-8B, with the objective of assessing their energy efficiency and performance in generating research highlights from scientific papers.

Key Findings and Contributions

1. Model Fine-tuning: The study fine-tunes three distinct models, each with different architectures and parameter counts. T5-base and BART-base, both pre-trained models, share a similar scale, while LLaMA 3-8B stands as a significantly larger LLM with billions of parameters. This contrast allows the authors to explore the relationship between model size and environmental impact.
2. Performance Metrics: The models were evaluated using a comprehensive set of metrics: ROUGE, METEOR, MoverScore, BERTScore, and SciBERTScore. T5-base and BART-base exhibited competitive performance, particularly in lexical overlap metrics like ROUGE and METEOR. However, LLaMA 3-8B showed improved semantic alignment in MoverScore and both variants of BERTScore, possibly indicating a preference for different phrasings rather than direct overlaps.
3. Energy and Environmental Impact Analysis: A pivotal aspect of this study is the quantification of energy consumption and carbon emissions during model fine-tuning. The methodology leverages established frameworks for estimating carbon footprints, considering multiple factors such as power consumption, hardware specifications, and geographic energy carbon intensity (CI). LLaMA 3-8B, while delivering strong performance, demonstrated a markedly higher carbon footprint due to its scale and complexity. The analysis also highlights that different data centers (based on CI) can further impact these results.
4. Implications for Sustainable AI: The paper underscores the need for balancing performance with environmental sustainability. It suggests incorporating energy efficiency as a core consideration in model selection, especially in resource-constrained or environmentally conscious settings. The study advocates for advancements in AI methodologies that prioritize greener practices without compromising performance.

Conclusion

The paper presents a thorough comparative analysis of the energy efficiency and performance capabilities of contemporary neural LLMs in NLP tasks, specifically focusing on text summarization. The results indicate intricate trade-offs between model size, effectiveness, and environmental impact. By spotlighting energy consumption as a critical factor, this research calls for concerted efforts to mitigate the ecological footprint of AI advancements, proposing that future work in this domain should integrate sustainability as a primary design and operational criterion. This study contributes to the ongoing discourse on "green AI" by providing empirical evidence and promoting methodological innovations that respect environmental limitations.