用大模型生成科学句的层级化JSON，能有效保留原文信息
Generating Hierarchical JSON Representations of Scientific Sentences Using LLMs
- 用新型结构损失函数微调轻量级LLM，将科学句子转为层级JSON
- 重建文本与原句在语义和词汇相似度上表现良好
- 适合需要结构化科学知识的场景，如文献分析、知识图谱
本文探究结构化表示能否保留科学句子的语义。为此，使用新型结构损失函数微调一个轻量级大模型，将来自科学论文的句子转化为层级化的JSON结构。随后，利用生成模型基于这些JSON重构原始文本。通过语义相似度和词汇相似度对比原句与重构句，结果表明层级格式能有效保留科学文本的信息。
原文摘要 · Abstract (English)
This paper investigates whether structured representations can preserve the meaning of scientific sentences. To test this, a lightweight LLM is fine-tuned using a novel structural loss function to generate hierarchical JSON structures from sentences collected from scientific articles. These JSONs are then used by a generative model to reconstruct the original text. Comparing the original and reconstructed sentences using semantic and lexical similarity we show that hierarchical formats are capable of retaining information of scientific texts effectively.
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