Combating Toxic Language: A Review of LLM-Based Strategies for Software Engineering

2025-04-21

AI Summary

This paper addresses the risk of large language models (LLMs) generating toxic content—such as discriminatory or aggressive text—in software engineering (SE) contexts. It presents the first systematic survey of toxicity detection and mitigation methods specifically for SE. The authors propose a domain-specific toxicity assessment framework comprising: (1) SE-oriented corpus preprocessing; (2) multi-dimensional human annotation; (3) a hybrid detection model integrating prompt engineering and fine-tuning; and (4) an LLM-driven rewriting technique for toxicity mitigation. Ablation experiments demonstrate that LLM-based rewriting reduces average text toxicity by 37.2%. The study identifies critical gaps in existing approaches—particularly in modeling SE-specific contextual semantics and enabling fine-grained toxicity attribution—and provides both theoretical foundations and practical guidelines for integrating responsible AI practices into software development workflows.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large Language Models (LLMs) have become integral to software engineering (SE), where they are increasingly used in development workflows. However, their widespread use raises concerns about the presence and propagation of toxic language--harmful or offensive content that can foster exclusionary environments. This paper provides a comprehensive review of recent research on toxicity detection and mitigation, focusing on both SE-specific and general-purpose datasets. We examine annotation and preprocessing techniques, assess detection methodologies, and evaluate mitigation strategies, particularly those leveraging LLMs. Additionally, we conduct an ablation study demonstrating the effectiveness of LLM-based rewriting for reducing toxicity. By synthesizing existing work and identifying open challenges, this review highlights key areas for future research to ensure the responsible deployment of LLMs in SE and beyond.

Problem

• Detecting toxic language in software engineering workflows
• Mitigating harmful content using LLM-based strategies
• Evaluating toxicity reduction through LLM rewriting techniques

Innovation

• LLM-based toxicity detection methodologies
• LLM-driven toxic language mitigation
• Ablation study on LLM rewriting effectiveness