Exploring Model Editing for LLM-based Aspect-Based Sentiment Classification

• URL: http://arxiv.org/abs/2503.15117v1
• Date: Wed, 19 Mar 2025 11:21:37 GMT
• Title: Exploring Model Editing for LLM-based Aspect-Based Sentiment Classification
• Authors: Shichen Li, Zhongqing Wang, Zheyu Zhao, Yue Zhang, Peifeng Li,
• Abstract summary: We investigate model editing to serve an efficient method for adapting large language models (LLMs) to solve aspect-based sentiment classification.<n>Our findings reveal that a distinct set of mid-layer representations is essential for detecting the sentiment polarity of given aspect words.<n>We develop a model editing approach that focuses exclusively on these critical parts of the LLM, leading to a more efficient method for adapting LLMs.
• Score: 17.512415475301395
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model editing aims at selectively updating a small subset of a neural model's parameters with an interpretable strategy to achieve desired modifications. It can significantly reduce computational costs to adapt to large language models (LLMs). Given its ability to precisely target critical components within LLMs, model editing shows great potential for efficient fine-tuning applications. In this work, we investigate model editing to serve an efficient method for adapting LLMs to solve aspect-based sentiment classification. Through causal interventions, we trace and determine which neuron hidden states are essential for the prediction of the model. By performing interventions and restorations on each component of an LLM, we identify the importance of these components for aspect-based sentiment classification. Our findings reveal that a distinct set of mid-layer representations is essential for detecting the sentiment polarity of given aspect words. Leveraging these insights, we develop a model editing approach that focuses exclusively on these critical parts of the LLM, leading to a more efficient method for adapting LLMs. Our in-domain and out-of-domain experiments demonstrate that this approach achieves competitive results compared to the currently strongest methods with significantly fewer trainable parameters, highlighting a more efficient and interpretable fine-tuning strategy.

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