UnBias-Plus: Detect, Explain, and Rewrite Bias
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Abstract (arXiv)
Bias in natural language remains a persistent challenge in both human-written and AI-generated content, affecting domains such as journalism, education, and AI research. Most existing detection methods identify only the presence of bias, with limited support for granular detection, interpretable explanations, neutral rewriting, and openly available trained models. We present UnBias-Plus, an open-source toolkit unifying (1) segment-level multi-class bias classification, (2) biased span localization, (3) neutral text rewriting, and (4) reasoning for each decision. Available via Python, CLI, REST API, and web interfaces, UnBias-Plus supports accessible bias analysis. The toolkit, source code, models, datasets, and documentation are publicly available.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 22 Jun 2026.
Builds on
6 of 8 references.
References · 6
- 2025Qwen3 Technical Reportcited by 8,820
- 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,207
- 2023Bias and Fairness in Large Language Models: A Surveycited by 639
- 2022Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Expertscited by 56
- 2025HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluationcited by 19
- 2026SONIC-O1: A Real-World Benchmark for Evaluating Multimodal Large Language Models on Audio-Video Understandingcited by 3
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Citing papers per year: 2026: 1.
1 paper.