Summary of Heuristic-enhanced Candidates Selection Strategy For Gpts Tackle Few-shot Aspect-based Sentiment Analysis, by Baoxing Jiang et al.
Heuristic-enhanced Candidates Selection strategy for GPTs tackle Few-Shot Aspect-Based Sentiment Analysis
by Baoxing Jiang, Yujie Wan, Shenggen Ju
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to Few-Shot Aspect-Based Sentiment Analysis (FSABSA) is introduced in this paper, which leverages a Heuristic-enhanced Candidates Selection (HCS) strategy and an All-in-One (AiO) model. The proposed method works in two stages: first, a backbone model based on Pre-trained Language Models (PLMs) generates rough heuristic candidates for the input sentence; then, AiO uses Large Language Model’s (LLMs’) contextual learning capabilities to generate precise predictions. The study conducts comprehensive comparative and ablation experiments on five benchmark datasets, demonstrating that the proposed model can better adapt to multiple sub-tasks and outperforms methods that directly utilize Generative Pre-trained Transformers (GPTs). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FSABSA is a challenging task in natural language processing that requires analyzing sentiment towards specific aspects of text. This paper proposes a new approach that combines the strengths of two types of models: those based on PLMs and those based on GPTs. The method uses a two-stage process to generate accurate predictions, first using a PLM-based backbone model and then fine-tuning with LLMs. The authors test their approach on five datasets and show that it outperforms existing methods. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Large language model » Natural language processing
