# Large Language Models Enhanced by Plug and Play Syntactic Knowledge for Aspect-based Sentiment Analysis

arXiv: https://arxiv.org/abs/2506.12991 · HTML: https://mlsanity.com/paper/2506.12991/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 49 references that are in the map.
- First posted on arXiv 15 Jun 2025.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1904.02232/) (2019)
- [Relational Graph Attention Network for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2004.12362/) (2020)
- [Effective LSTMs for Target-Dependent Sentiment Classification](https://mlsanity.com/paper/1512.01100/) (2015)
- [Aspect Based Sentiment Analysis with Gated Convolutional Networks](https://mlsanity.com/paper/1805.07043/) (2018)
- [Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks](https://mlsanity.com/paper/1909.03477/) (2019)
- [Transformation Networks for Target-Oriented Sentiment Classification](https://mlsanity.com/paper/1805.01086/) (2018)
- [Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks](https://mlsanity.com/paper/1909.02606/) (2019)
- [A Survey on Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2203.14266/) (2022)
- [Large language models for aspect-based sentiment analysis](https://mlsanity.com/paper/2310.18025/) (2023)
- [BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2204.03117/) (2022)
- [An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2004.01935/) (2020)
- [Context-aware Embedding for Targeted Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1906.06945/) (2019)
- [Exploring Model Editing for LLM-based Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2503.15117/) (2025)
- [ZZU-NLP at SIGHAN-2024 dimABSA Task: Aspect-Based Sentiment Analysis with Coarse-to-Fine In-context Learning](https://mlsanity.com/paper/2407.15341/) (2024)
- [Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing](https://mlsanity.com/paper/2506.07086/) (2025)

## Built on by (most cited first, 3 of 3)

- [Text Reinforcement for Multimodal Time Series Forecasting](https://mlsanity.com/paper/2509.00687/) (2025)
- [Fusing Large Language Models with Temporal Transformers for Time Series Forecasting](https://mlsanity.com/paper/2507.10098/) (2025)
- [Balanced Training Data Augmentation for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2507.09485/) (2025)

## Abstract (arXiv)

Aspect-based sentiment analysis (ABSA) generally requires a deep understanding of the contextual information, including the words associated with the aspect terms and their syntactic dependencies. Most existing studies employ advanced encoders (e.g., pre-trained models) to capture such context, especially large language models (LLMs). However, training these encoders is resource-intensive, and in many cases, the available data is insufficient for necessary fine-tuning. Therefore it is challenging for learning LLMs within such restricted environments and computation efficiency requirement. As a result, it motivates the exploration of plug-and-play methods that adapt LLMs to ABSA with minimal effort. In this paper, we propose an approach that integrates extendable components capable of incorporating various types of syntactic knowledge, such as constituent syntax, word dependencies, and combinatory categorial grammar (CCG). Specifically, we propose a memory module that records syntactic information and is incorporated into LLMs to instruct the prediction of sentiment polarities. Importantly, this encoder acts as a versatile, detachable plugin that is trained independently of the LLM. We conduct experiments on benchmark datasets, which show that our approach outperforms strong baselines and previous approaches, thus demonstrates its effectiveness.