"How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning"
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https://arxiv.org/abs/2501.15398
The increasing use of LLMs poses a significant environmental challenge due to their high energy consumption during training and fine-tuning. This paper addresses the critical need to understand the energy implications of using different LLMs for Natural Language Processing tasks.
This paper explores the energy consumption and performance trade-offs when fine-tuning different sized language models for research highlight generation. The study compares two smaller pre-trained language models and a larger LLM to analyze their environmental impact alongside task performance.
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📌 Smaller pre-trained language models like BART-base and T5-base offer a computationally cheaper and environmentally friendlier alternative. They achieve comparable text summarization quality to larger LLMs when semantic evaluation is considered.
📌 Traditional metrics like ROUGE might undervalue LLMs' abstractive capabilities. Semantic metrics such as BERTScore reveal that LLaMA-3-8B's summarization quality is closer to smaller models than ROUGE suggests.
📌 This paper quantifies the carbon cost of LLM fine-tuning, showing LLaMA-3-8B's footprint is significantly higher (43.98 gCO2e per epoch) than smaller models. This underscores the environmental impact of choosing larger models.
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Methods Explored in this Paper 🔧:
→ The paper fine-tunes three language models for text summarization: T5-base, BART-base, and LLaMA-3-8B.
→ MixSub dataset of research paper abstracts and author-written highlights is used for fine-tuning and evaluation.
→ Performance is evaluated using ROUGE, METEOR, MoverScore, BERTScore, and SciBERTScore metrics.
→ Energy consumption and carbon footprint are calculated using the Green Algorithms calculator and a standard carbon footprint estimation equation.
→ The carbon footprint calculation considers CPU, GPU, memory usage, Power Usage Effectiveness (PUE), and Carbon Intensity (CI).
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Key Insights 💡:
→ Smaller models like T5-base and BART-base achieve comparable or better performance than LLaMA-3-8B on traditional metrics like ROUGE and METEOR.
→ LLaMA-3-8B, while semantically comparable on metrics like BERTScore, consumes significantly more energy during fine-tuning due to its larger size.
→ The study highlights a trade-off: larger LLMs are more computationally expensive and environmentally impactful, while smaller models can offer competitive performance for specific tasks.
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Results 📊:
→ T5-base achieved ROUGE-1 of 32.91 and METEOR of 29.94.
→ BART-base achieved ROUGE-1 of 34.28 and METEOR of 28.81.
→ LLaMA-3-8B achieved ROUGE-1 of 27.41 and METEOR of 23.73.
→ Fine-tuning one epoch of T5-base produces 2.4 gCO2e carbon footprint.
→ Fine-tuning one epoch of BART-base produces 3.5 gCO2e carbon footprint.
→ Fine-tuning one epoch of LLaMA-3-8B produces 43.98 gCO2e carbon footprint.