Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints
This addresses the challenge of effectively integrating semantic information into LLMs for enhanced performance, though it appears incremental as it adapts existing semantic parsing concepts to LLMs.
The paper tackles the problem of semantic parsing for large language models (LLMs), finding that directly adding semantic parsing results reduces LLM performance, and proposes SENSE, a prompting approach with semantic hints that improves LLM performance across various tasks.
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.