Reasoning before Comparison: LLM-Enhanced Semantic Similarity Metrics for Domain Specialized Text Analysis
<|reference_start|>Reasoning before Comparison: LLM-Enhanced Semantic Similarity Metrics for Domain Specialized Text Analysis: In this study, we leverage LLM to enhance the semantic analysis and develop similarity metrics for texts, addressing the limitations of traditional unsupervised NLP metrics like ROUGE and BLEU. We develop a framework where LLMs such as GPT-4 are employed for zero-shot text identification and label generation for radiology reports, where the labels are then used as measurements for text similarity. By testing the proposed framework on the MIMIC data, we find that GPT-4 generated labels can significantly improve the semantic similarity assessment, with scores more closely aligned with clinical ground truth than traditional NLP metrics. Our work demonstrates the possibility of conducting semantic analysis of the text data using semi-quantitative reasoning results by the LLMs for highly specialized domains. While the framework is implemented for radiology report similarity analysis, its concept can be extended to other specialized domains as well.<|reference_end|>
@article{xu2024reasoning,
    title={Reasoning before Comparison: LLM-Enhanced Semantic Similarity Metrics
  for Domain Specialized Text Analysis},
    author={Shaochen Xu, Zihao Wu, Huaqin Zhao, Peng Shu, Zhengliang Liu, Wenxiong
  Liao, Sheng Li, Andrea Sikora, Tianming Liu, Xiang Li},
    journal={arXiv preprint arXiv:2402.11398},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11398},
    primaryClass={cs.CL cs.AI}
}