用短文本筛选提升科学论文分类效率，兼顾精度与速度。
Efficient Scientific Full Text Classification: The Case of EICAT Impact Assessments
- 通过人类标注、LLM生成或可解释性评分筛选关键句子，减少输入长度。
- 在入侵生物学论文上，新方法使小模型性能超越完整文本输入的ModernBERT。
- 重复采样短输入能进一步提升效果，适合资源有限的研究场景。
本研究探索利用小型BERT模型和本地大语言模型（如Llama-3.1 8B）高效分类科学全文的方法。重点在于通过选择输入句子子集来减少输入规模，同时提升分类性能。为此，我们构建了一个新的数据集，包含入侵生物学领域的全篇科学论文，这些论文与国际自然保护联盟（IUCN）公开发布的物种入侵影响评估对齐。通过大量实验表明，人类证据标注、LLM生成标注或可解释性得分等多种来源可用于训练句子选择模型，能够提升编码器与解码器类语言模型的性能，并通过缩短输入长度优化效率，其结果甚至优于能处理完整文本的ModernBERT模型。此外，发现对较短输入进行重复采样是一种极为有效策略，在稍高成本下可进一步提升分类表现。
原文摘要 · Abstract (English)
This study explores strategies for efficiently classifying scientific full texts using both small, BERT-based models and local large language models like Llama-3.1 8B. We focus on developing methods for selecting subsets of input sentences to reduce input size while simultaneously enhancing classification performance. To this end, we compile a novel dataset consisting of full-text scientific papers from the field of invasion biology, specifically addressing the impacts of invasive species. These papers are aligned with publicly available impact assessments created by researchers for the International Union for Conservation of Nature (IUCN). Through extensive experimentation, we demonstrate that various sources like human evidence annotations, LLM-generated annotations or explainability scores can be used to train sentence selection models that improve the performance of both encoder- and decoder-based language models while optimizing efficiency through the reduction in input length, leading to improved results even if compared to models like ModernBERT that are able to handle the complete text as input. Additionally, we find that repeated sampling of shorter inputs proves to be a very effective strategy that, at a slightly increased cost, can further improve classification performance.
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