Learning Evaluation Models from Large Language Models for Sequence Generation

• URL: http://arxiv.org/abs/2308.04386v1
• Date: Tue, 8 Aug 2023 16:41:16 GMT
• Title: Learning Evaluation Models from Large Language Models for Sequence Generation
• Authors: Chenglong Wang, Hang Zhou, Kaiyan Chang, Tongran Liu, Chunliang Zhang, Quan Du, Tong Xiao, Jingbo Zhu
• Abstract summary: Large language models achieve state-of-the-art performance on sequence generation evaluation, but typically have a large number of parameters. We propose textbfECT, an textbfevaluation textbfcapability textbftransfer method, to transfer the evaluation capability from LLMs to relatively lightweight language models. Based on the proposed ECT, we learn various evaluation models from ChatGPT, and employ them as reward models to improve sequence generation models.
• Score: 44.22820310679188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models achieve state-of-the-art performance on sequence generation evaluation, but typically have a large number of parameters. This is a computational challenge as presented by applying their evaluation capability at scale. To overcome the challenge, in this paper, we propose \textbf{ECT}, an \textbf{e}valuation \textbf{c}apability \textbf{t}ransfer method, to transfer the evaluation capability from LLMs to relatively lightweight language models. Based on the proposed ECT, we learn various evaluation models from ChatGPT, and employ them as reward models to improve sequence generation models via reinforcement learning and reranking approaches. Experimental results on machine translation, text style transfer, and summarization tasks demonstrate the effectiveness of our ECT. Notably, applying the learned evaluation models to sequence generation models results in better generated sequences as evaluated by commonly used metrics and ChatGPT.

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