Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text

TL;DR

This paper introduces a multi-level knowledge distillation method that combines fine-tuned and pre-trained models to improve out-of-distribution detection in text, achieving state-of-the-art results across multiple benchmarks.

Contribution

It proposes a novel multi-level knowledge distillation approach that integrates prediction and intermediate layer distillation to enhance OoD detection capabilities.

Findings

Achieves new state-of-the-art performance on multiple benchmark datasets.

Effectively distinguishes between in-distribution and out-of-distribution texts.

Outperforms human evaluators in detecting ChatGPT-generated answers.

Abstract

Self-supervised representation learning has proved to be a valuable component for out-of-distribution (OoD) detection with only the texts of in-distribution (ID) examples. These approaches either train a language model from scratch or fine-tune a pre-trained language model using ID examples, and then take the perplexity output by the language model as OoD scores. In this paper, we analyze the complementary characteristics of both OoD detection methods and propose a multi-level knowledge distillation approach that integrates their strengths while mitigating their limitations. Specifically, we use a fine-tuned model as the teacher to teach a randomly initialized student model on the ID examples. Besides the prediction layer distillation, we present a similarity-based intermediate layer distillation method to thoroughly explore the representation space of the teacher model. In this way, the learned student can better represent the ID data manifold while gaining a stronger ability to map OoD examples outside the ID data manifold with the regularization inherited from pre-training. Besides, the student model sees only ID examples during parameter learning, further promoting more distinguishable features for OoD detection. We conduct extensive experiments over multiple benchmark datasets, i.e., CLINC150, SST, ROSTD, 20 NewsGroups, and AG News; showing that the proposed method yields new state-of-the-art performance. We also explore its application as an AIGC detector to distinguish between answers generated by ChatGPT and human experts. It is observed that our model exceeds human evaluators in the pair-expert task on the Human ChatGPT Comparison Corpus.