Aligning Black-Box LLMs for Aspect Sentiment Quad Prediction

Shichen Li; Jiawei Zhang; Zhongqing Wang; Peifeng Li

EMNLP 20250 citations

Aligning Black-Box LLMs for Aspect Sentiment Quad Prediction

Shichen Li, Jiawei Zhang, Zhongqing Wang, Peifeng Li

Abstract

Aspect-Based Sentiment Analysis (ABSA) focuses on extracting opinions about specific aspects, with Aspect Sentiment Quad Prediction (ASQP) being the most complex sub-task. Large language models (LLMs) like GPT4 exhibit strong generalization yet struggle with ASQP due to a lack of task-specific alignment. Supervised small language models (SLMs), while effective in capturing task-specific patterns, lack the extensive knowledge of LLMs. To address this, we propose a framework that combines SLMs and LLMs using supervised in-context learning to align LLM outputs with human preferences. One SLM is supervised to generate candidate answers and guide LLMs with task-specific instructions, while another SLM acts as a reward model iteratively evaluates and refines LLM outputs. Experiments show that our framework significantly improves ASQP performance, demonstrating robustness, scalability, and potential for advancing alignment techniques in sentiment analysis.

BibTeX

@inproceedings{emnlp2025_aligningblackbox,
  title = {Aligning Black-Box LLMs for Aspect Sentiment Quad Prediction},
  author = {Shichen Li and Jiawei Zhang and Zhongqing Wang and Peifeng Li},
  booktitle = {EMNLP 2025},
  year = {2025}
}