Gazzali, MD. FakhruddinAzmain, Md. AquibSadeque, Farig YousufParvez, Md FardinRashik, Aswadul KarimDeepto, MD Yameem DaiyanHaq, Mostakim UlKhan, Alex Noor2025-09-162025-09-1620252025-06ID 23341099ID 24341250ID 21241052ID 23241080ID 18241005http://hdl.handle.net/10361/26753Cataloged from PDF version of thesis.Includes bibliographical references (pages 39-42).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2025.The surge of toxic interactions online poses a serious obstacle to cultivating constructive digital spaces. Current automated detoxification systems, though encouraging in theory, often stumble: they veer off-topic (semantic drift), strip away too much nuance (over-detoxification), and operate like black boxes, eroding trust and complicating real-world use. In response, this paper outlines a detoxification pipeline designed to tackle these issues head-on by weaving explicit reasoning into grounded generation. Our proposed pipeline particularly introduces a multi-task setup that finetunes a 7-billion-parameter language model in two stages: first, it produces a clear explanation of why a comment is toxic (drawing on the concept of Chain-of-Thought prompting), and then it rewrites the comment. What sets our approach apart is a three-step inference routine: the explanation guides a retrieval query that fetches relevant, benign examples from a curated knowledge base via Retrieval-Augmented Generation. The final rewrite leans on both the rationale and these examples, anchoring the output in a transparent reasoning path and verified data. To bolster robustness, we merge data from over 40,000 cases across three sources—synthetic examples with reasoning (DetoxLLM), human-crafted paraphrases (ParaDetox), and adversarial hate speech instances (ToxiGen)—using a fresh fusion strategy. Crucially, we make the fine-tuning doable on everyday hardware by employing 4-bit Quantized Low-Rank Adaptation (QLoRA). Experiments show that our reasoningplus- retrieval pipeline is a feasible and effective alternative at retaining the original meaning while achieving strong style-transfer metrics. By emphasising not only performance but also clarity and fidelity, this system charts a path toward more accountable, inspectable moderation tools.52 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Chain-of-thought reasoningText detoxificationLarge language modelsBias mitigationLLM-driven detoxificationNatural language processing (Computer science).Computational linguistics.Text processing (Computer science).Content moderation (Social media).Thesis