@inproceedings{an-etal-2025-rethinking,
    title = "Rethinking Semantic Parsing for Large Language Models: Enhancing {LLM} Performance with Semantic Hints",
    author = "An, Kaikai  and
      Si, Shuzheng  and
      Hu, Helan  and
      Zhao, Haozhe  and
      Wang, Yuchi  and
      Guo, Qingyan  and
      Chang, Baobao",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-short.79/",
    pages = "1020--1029",
    ISBN = "979-8-89176-252-7",
    abstract = "Semantic Parsing aims to capture the meaning of a sentence and convert it into a logical, structured form. Previous studies show that semantic parsing enhances the performance of smaller models (e.g., BERT) on downstream tasks. However, it remains unclear whether the improvements extend similarly to LLMs. In this paper, our empirical findings reveal that, unlike smaller models, directly adding semantic parsing results into LLMs reduces their performance. To overcome this, we propose SENSE, a novel prompting approach that embeds semantic hints within the prompt. Experiments show that SENSE consistently improves LLMs' performance across various tasks, highlighting the potential of integrating semantic information to improve LLM capabilities."
}