Approximating Human Evaluation of Social Chatbots with Prompting
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Once powerful conversational models have become available for a wide audience, users started actively engaging in social interactions with this technology. Such unprecedented interaction experiences may pose considerable social and psychological risks to the users unless the technology is properly controlled. This creates an urgent need for scalable and robust evaluation metrics for conversational chatbots. Existing automatic evaluation metrics usually focus on objective quality measures and disregard subjective perceptions of social dimensions. Moreover, most of these approaches operate on pre-produced dialogs from available benchmark corpora, which implies human involvement for preparing the material for evaluation and, thus, impeded scalability of the metrics. To address this limitation, we propose to make use of the emerging large language models (LLMs) from the GPT-family and describe a new framework allowing to conduct dialog system evaluation with prompting. With this framework, we are able to achieve full automation of the evaluation pipeline and reach impressive correlation with the human judgement (up to Pearson r=0.95 on system level). The underlying concept is to collect synthetic chat logs of evaluated bots with a LLM in the other-play setting, where LLM is carefully conditioned to follow a specific scenario. We further explore different prompting approaches to produce evaluation scores with the same LLM. The best-performing prompts, containing few-show demonstrations and instructions, show outstanding performance on the tested dataset and demonstrate the ability to generalize to other dialog corpora.
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