The current largest released LM (GPT-J-6B) using prompt-based few-shot learning, and thus requiring no training, achieves competitive performance to fully trained state-of-the-art models. We benchmark LMs of different sizes in nine response generation tasks, which include four knowledge-grounded tasks, a task-oriented generations task, three open-chat tasks, and controlled stylistic generation, and five conversational parsing tasks, which include dialogue state tracking, graph path generation, persona information extraction, document retrieval, and internet query generation. In this paper, we explore prompt-based few-shot learning in dialogue tasks. 2020) which does not require gradient-based fine-tuning but instead uses a few examples in the LM context as the only source of learning. A simple yet unexplored solution is prompt-based few-shot learning (Brown et al. Training these models is expensive, both in terms of computational resources and time, and it is hard to keep them up to date with new conversational skills.
The current best conversational models, which are either good chit-chatters (e.g., BlenderBot) or goal-oriented systems (e.g., MinTL), are language models (LMs) fine-tuned on large conversational datasets. Learning to converse using only a few examples is a great challenge in conversational AI.
#Shotbot competitors code#
This repository includes the dataset, experiments results, and code for the paper:įew-Shot Bot: Prompt-Based Learning for Dialogue Systems PDF.Īuthors: Andrea Madotto, Zhaojiang Lin, Genta Indra Winata, Pascale Fung Abstract Few-Shot Bot: Prompt-Based Learning for Dialogue Systems
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