Measuring and Mitigating Toxicity in Large Language Models: A Comprehensive Replication Study
Large Language Models (LLMs) trained on web-scale corpora inherently absorb toxic patterns from their training data. This leads to toxic degeneration where even innocuous prompts can trigger harmful outputs. This phenomenon poses significant risks for real-world deployments. Thus, necessitating effective mitigation strategies that should maintain model utility while ensuring safety. In this comprehensive replication study, we evaluate the efficacy of DExperts (Decoding-time Experts), which is an inference-time mitigation technique that steers generation without requiring model retraining. We structured our research into three systematic phases: (1) establishing baseline toxicity measureme...
| Authors | Mokshit Surana, Archit Rathod, Akshaj Satishkumar |
|---|---|
| Primary category | cs.CL |
| Published | 13 May 2026 |
| Last updated at source | 15 May 2026 |
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