Beyond Self-learned Attention: Mitigating Attention Bias in Transformer-based Models Using Attention Guidance

• URL: http://arxiv.org/abs/2402.16790v1
• Date: Mon, 26 Feb 2024 18:03:50 GMT
• Title: Beyond Self-learned Attention: Mitigating Attention Bias in Transformer-based Models Using Attention Guidance
• Authors: Jiri Gesi and Iftekhar Ahmed
• Abstract summary: We introduce SyntaGuid, a novel approach to guide Transformer-based models towards critical source code tokens. We show that SyntaGuid can improve overall performance up to 3.25% and fix up to 28.3% wrong predictions.
• Score: 9.486558126032639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based models have demonstrated considerable potential for source code modeling tasks in software engineering. However, they are limited by their dependence solely on automatic self-attention weight learning mechanisms. Previous studies have shown that these models overemphasize delimiters added by tokenizers (e.g., [CLS], [SEP]), which may lead to overlooking essential information in the original input source code. To address this challenge, we introduce SyntaGuid, a novel approach that utilizes the observation that attention weights tend to be biased towards specific source code syntax tokens and abstract syntax tree (AST) elements in fine-tuned language models when they make correct predictions. SyntaGuid facilitates the guidance of attention-weight learning, leading to improved model performance on various software engineering tasks. We evaluate the effectiveness of SyntaGuid on multiple tasks and demonstrate that it outperforms existing state-of-the-art models in overall performance without requiring additional data. Experimental result shows that SyntaGuid can improve overall performance up to 3.25% and fix up to 28.3% wrong predictions. Our work represents the first attempt to guide the attention of Transformer-based models towards critical source code tokens during fine-tuning, highlighting the potential for enhancing Transformer-based models in software engineering.

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