用小模型高效生成气候报告摘要，兼顾性能与低碳环保。
Efficient Aspect-Based Summarization of Climate Change Reports with Small Language Models
- 采用无监督方法，用小语言模型处理气候报告的方面摘要任务。
- 小模型在摘要质量上接近大模型，碳足迹显著降低。
- 首次将能效与性能结合评估零样本生成模型，适合可持续研究者。
自然语言处理技术在支持气候行动决策方面的应用日益受到关注，符合推动NLP服务社会的总体趋势。在此背景下，面向特定方面的摘要（ABS）系统尤为关键，可帮助利益相关方从专家编纂的报告中快速获取相关信息。本文发布了一个新的气候报告ABS数据集，并使用大型语言模型（LLMs）和小型语言模型（SLMs）以无监督方式解决该问题。我们首次将同时考虑能源效率与任务性能的评估框架应用于零样本生成模型的ABS评估，结果表明：尽管小模型在性能上略有下降，但其碳足迹大幅减少。整体结果显示，现代语言模型（无论大小）均可有效应对气候报告的方面摘要任务，但若将问题建模为检索增强生成（RAG）形式，则仍需更多研究。本工作及数据集将推动该方向的研究进展。
原文摘要 · Abstract (English)
The use of Natural Language Processing (NLP) for helping decision-makers with Climate Change action has recently been highlighted as a use case aligning with a broader drive towards NLP technologies for social good. In this context, Aspect-Based Summarization (ABS) systems that extract and summarize relevant information are particularly useful as they provide stakeholders with a convenient way of finding relevant information in expert-curated reports. In this work, we release a new dataset for ABS of Climate Change reports and we employ different Large Language Models (LLMs) and so-called Small Language Models (SLMs) to tackle this problem in an unsupervised way. Considering the problem at hand, we also show how SLMs are not significantly worse for the problem while leading to reduced carbon footprint; we do so by applying for the first time an existing framework considering both energy efficiency and task performance to the evaluation of zero-shot generative models for ABS. Overall, our results show that modern language models, both big and small, can effectively tackle ABS for Climate Change reports but more research is needed when we frame the problem as a Retrieval Augmented Generation (RAG) problem and our work and dataset will help foster efforts in this direction.
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