bibtext: @inproceedings{
  li2024destein,
  title={DeStein: Navigating Detoxification of Language Models via Universal Steering Pairs and Head-wise Activation Fusion},
  author={Yu Li and Han Jiang and Chuanyang Gong and Zhihua Wei},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=jq2kNXigPP}
  }
abstract: Despite the remarkable achievements of language models (LMs) across a broad spectrum of tasks, their propensity for generating toxic outputs remains a prevalent concern. Current solutions involving finetuning or auxiliary models usually require extensive computational resources, hindering their practicality in large language models (LLMs). In this paper, we propose DeStein, a novel method that detoxifies LMs by applying representation engineering in activation spaces with lower resource and time costs. Specifically, we derive detoxification vectors from self-induced, universal steering pairs through arithmetic operations in activation spaces. During inference, detoxification is achieved by fusing the detoxification vectors with the original representations in a head-wise manner. Empirical results demonstrate that our method significantly outperforms previous state-of-the-art approaches on various metrics, while also maintaining satisfactory generation quality and diversity. We further validate the practicality and scalability of DeStein with a series of white-box LLMs. Warning: Some example model outputs may contain highly offensive or disturbing text.
title: DeStein: Navigating Detoxification of Language Models via Universal Steering Pairs and Head-wise Activation Fusion
authors: Yu Li Han Jiang Chuanyang Gong Zhihua Wei