CCDE: A Compact and Competitive Dialogue Evaluation Framework via Knowledge Distillation of Large Language Models

Automatic evaluation metrics not only play a vital role in developing dialogue and interactive systems but also have a great impact on social activities in our daily life. However, previous specialized metrics for evaluating dialogues exhibit a relatively low correlation with human judgments. In addition, today’s state-of-the-art (SOTA) evaluators that leverage large language models (LLMs) are challenging to deploy in real-world applications due to their sheer size. To this end, we propose a novel evaluation framework, compact and competitive dialogue evaluation (CCDE), which leverages knowledge distillation of LLMs to generate training data and sequentially learn a multitask evaluator regarding diversified quality dimensions. Specifically, we first employ ChatGPT as teacher to generate a high-quality and rich-annotation corpus, CCDE-data. Then, we implement a student evaluator CCDE (1.3B) via using InstructGPT as the backbone model that is trained and fine-tuned on CCDE-data. We conduct extensive experiments on three public benchmarks: fine-grained evaluation of dialog (FED), PersonaChat, and TopicalChat. The results demonstrate that our model CCDE can outperform the current SOTA model G-Eval which calls GPT-4 ($\boldsymbol{\geq}$ 175B) by 4.3 on the FED dataset, 3.5 on the PersonaChat dataset, and 0.3 on the TopicalChat dataset, in terms of the Spearman correlation metric (%). We release the data and code at: https://anonymous.4open.science/r/ccde-3827.