Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints

• URL: http://arxiv.org/abs/2409.14469v1
• Date: Sun, 22 Sep 2024 14:35:09 GMT
• Title: Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints
• Authors: Kaikai An, Shuzheng Si, Helan Hu, Haozhe Zhao, Yuchi Wang, Qingyan Guo, Baobao Chang,
• Abstract summary: 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.
• Score: 20.844061807562436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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