S²IT: Stepwise Syntax Integration Tuning for LLMs in Aspect Sentiment Quad Prediction
A new framework called S²IT (Stepwise Syntax Integration Tuning) has been proposed to enhance large language models (LLMs) for Aspect Sentiment Quad Prediction (ASQP). While LLMs excel in semantic understanding, they struggle to incorporate syntactic structure information in generative paradigms. S²IT addresses this through a multi-step tuning process that decomposes quadruple generation into two stages: Global Syntax-guided Extraction and Local Syntax-guided Classification, followed by Fine-grained Structural Tuning. The framework progressively integrates both global and local syntactic knowledge, aiming to improve the model's reasoning and structural understanding. The paper is available on arXiv.
Key facts
- S²IT is a novel framework for integrating syntactic structure into LLMs for ASQP.
- The training process consists of three steps.
- Quadruple generation is decomposed into two stages: Global Syntax-guided Extraction and Local Syntax-guided Classification.
- Fine-grained Structural Tuning enhances understanding of syntactic structure.
- The paper is published on arXiv with ID 2604.23296.
- Syntactic information has been effective in extractive paradigms but underutilized in generative LLMs.
- The framework aims to improve LLM reasoning capabilities for ASQP.
- The approach is stepwise and progressively integrates syntactic knowledge.
Entities
Institutions
- arXiv