Self‐Adaptive LLM Instructions Optimization for Aspect‐Based Sentiment Analysis by Incorporating Emotion‐Oriented In‐Contexts
Weiqiang Jin, Junli Wang, Yang Gao, Bohang Shi, Ningwei Wang, Biao Zhao, Guang Yang
Abstract
ABSTRACT Aspect‐based Sentiment Analysis (ABSA) is a vital NLP task that identifies sentiment towards specific entities or aspect terms within a text. Recently, large language models (LLMs) have shown impressive capabilities in semantic comprehension and logical inference. However, LLM hallucinations pose challenges in accurately determining sentiment polarity for aspect terms, leading to performance issues. Moreover, current ABSA methods often fail to fully leverage the vast prior knowledge embedded within LLMs, resulting in suboptimal classification outcomes for specific aspects. Inspired by these challenges, we propose the BYD‐OBS‐ABSA framework—‘Beyond Simple Observations, Embracing Comprehensive Contextual Insights’ for ABSA tasks. This framework leverages unique in‐context constraints, backgrounds, and analogical reasoning to address LLM hallucinations and uses self‐adaptive bootstrap instructions optimization to enhance LLM predictions. BYD‐OBS‐ABSA integrates various in‐context augmentation strategies, including emotion‐oriented backgrounds, constraints, and analogical reasoning. BYD‐OBS‐ABSA further improves initial LLM instructions through adaptive iterative optimization using a random search bootstrap algorithm, maximizing the benefits of LLM prompting. Extensive zero/few‐shot experiments with GPT‐3.5‐turbo across six public datasets validate the effectiveness and robustness of our framework, even surpassing human judgment in certain scenarios.