Papers with ParaDetox
CausalDetox: Causal Head Selection and Intervention for Language Model Detoxification (2026.findings-acl)
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| Challenge: | Large language models (LLMs) frequently generate toxic content, posing significant risks for safe deployment. |
| Approach: | They propose a framework that identifies and intervenes on the specific attention heads causally responsible for toxic generation. |
| Outcome: | The proposed framework reduces toxic generation by 5.34% while preserving linguistic fluency and speeding up head selection. |
COUNT: COntrastive UNlikelihood Text Style Transfer for Text Detoxification (2023.findings-emnlp)
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Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Manasa Bharadwaj, Nikhil Verma, Ali Pesaranghader, Scott Sanner
| Challenge: | Text detoxification is a task to ensure the generation of non-toxic and safe text. |
| Approach: | They propose a novel contrastive unlikelihood objective that combines rephrasing and identity mapping to effectively isolate and focus learning on non-toxic style transfer. |
| Outcome: | The proposed method achieves significant improvements in fluency, content preservation, and detoxification on two parallel datasets. |
LLMs to Replace Crowdsourcing For Parallel Data Creation? The Case of Text Detoxification (2024.findings-emnlp)
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| Challenge: | Manual annotation methods, such as crowdsourcing, are costly and require intricate task design skills. |
| Approach: | They propose to use open source LLMs to annotate parallel data for text detoxification . they generate a pseudo-parallel detoxification dataset using activation patching . |
| Outcome: | The proposed model performs comparable to the original dataset in automatic detoxification evaluation metrics and superior quality in manual evaluation and side-by-side comparisons. |
SUE: Sparsity-based Uncertainty Estimation via Sparse Dictionary Learning (2025.emnlp-main)
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| Challenge: | Existing methods to estimate uncertainty use predictive confidence, structural characteristics of representation space, or stochastic variation in model outputs. |
| Approach: | They propose a new uncertainty estimation framework based on sparse dictionary learning by identifying dictionary atoms associated with misclassified samples. |
| Outcome: | The proposed framework outperforms or matches existing methods on several NLU benchmarks and sentiment analysis benchmarks. |