Efficient Toxic Content Detection by Bootstrapping and Distilling Large Language Models
This addresses the challenge of deploying efficient and accurate toxic content detection systems for online services, offering a method to reduce computational costs while maintaining performance, though it is incremental in improving existing distillation techniques.
The paper tackles the problem of efficiently detecting toxic content online by proposing BD-LLM, which bootstraps and distills large language models (LLMs) using a novel prompting method called Decision-Tree-of-Thought (DToT) to improve accuracy and transferability, resulting in up to 4.6% accuracy improvement for LLMs and up to 16.9% accuracy improvement for smaller student models that are over 60x smaller.
Toxic content detection is crucial for online services to remove inappropriate content that violates community standards. To automate the detection process, prior works have proposed varieties of machine learning (ML) approaches to train Language Models (LMs) for toxic content detection. However, both their accuracy and transferability across datasets are limited. Recently, Large Language Models (LLMs) have shown promise in toxic content detection due to their superior zero-shot and few-shot in-context learning ability as well as broad transferability on ML tasks. However, efficiently designing prompts for LLMs remains challenging. Moreover, the high run-time cost of LLMs may hinder their deployments in production. To address these challenges, in this work, we propose BD-LLM, a novel and efficient approach to Bootstrapping and Distilling LLMs for toxic content detection. Specifically, we design a novel prompting method named Decision-Tree-of-Thought (DToT) to bootstrap LLMs' detection performance and extract high-quality rationales. DToT can automatically select more fine-grained context to re-prompt LLMs when their responses lack confidence. Additionally, we use the rationales extracted via DToT to fine-tune student LMs. Our experimental results on various datasets demonstrate that DToT can improve the accuracy of LLMs by up to 4.6%. Furthermore, student LMs fine-tuned with rationales extracted via DToT outperform baselines on all datasets with up to 16.9\% accuracy improvement, while being more than 60x smaller than conventional LLMs. Finally, we observe that student LMs fine-tuned with rationales exhibit better cross-dataset transferability.