TUM-NLPLab-2022/PARL-A-Dialog-System-Framework-with-Prompts-as-Actions-for-Reinforcement-Learning
This is the offical repo for the paper "PARL: A Dialog System Framework with Prompts as Actions for Reinforcement Learning" at ICAART 2023. https://www.scitepress.org/PublicationsDetail.aspx?ID=nLEpnPjrvCI=&t=1
This framework helps AI researchers and developers build more effective conversational AI agents. It uses prompt-based reinforcement learning to train dialogue systems, taking a dataset of conversational examples and producing an AI that can generate more nuanced and engaging responses. This is for professionals creating or evaluating advanced chatbots and virtual assistants.
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Use this if you are a researcher or developer aiming to train a dialogue system that learns to generate conversational responses through reinforcement learning, leveraging prompt-based actions.
Not ideal if you are a non-technical user looking for an off-the-shelf chatbot or a simple API to integrate basic conversational capabilities.
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Python
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MIT
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Last pushed
Jun 10, 2023
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