A dual-refinement method (DRE) that uses a contrastively trained small language model to guide and rescale an LLM's dialogue quality scores achieves higher correlation with human ratings than LLM-only baselines on three datasets.
In Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023), pages 47–58, Association for Computational Linguistics, Toronto, Canada
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DRE: An Effective Dual-Refined Method for Integrating Small and Large Language Models in Open-Domain Dialogue Evaluation
A dual-refinement method (DRE) that uses a contrastively trained small language model to guide and rescale an LLM's dialogue quality scores achieves higher correlation with human ratings than LLM-only baselines on three datasets.