Summary of Detoxllm: a Framework For Detoxification with Explanations, by Md Tawkat Islam Khondaker et al.
DetoxLLM: A Framework for Detoxification with Explanations
by Md Tawkat Islam Khondaker, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY)
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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 This paper proposes a comprehensive end-to-end detoxification framework called DetoxLLM that addresses limitations in prior works by developing models that can perform well on unseen platforms. The authors introduce a cross-platform pseudo-parallel corpus using ChatGPT and train a suite of detoxification models to outperform the state-of-the-art (SoTA) model trained with human-annotated parallel data. Additionally, they provide explanation mechanisms for transparency and trustworthiness, as well as a paraphrase detector specifically designed for the detoxification task. The framework’s effectiveness is demonstrated through experimental analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make online text less toxic. It’s like a superpower for making sure what you read or write isn’t mean-spirited. Right now, there are problems with how we do this kind of thing. Some methods only work on certain websites and don’t handle cases where the original message is too important to change. This paper tries to fix those issues by creating a better way to make text less toxic that works everywhere and keeps the original message intact. They also made some tools to help people understand how their detoxification models are working, which is important for building trust in these kinds of AI systems. |
