Af1tang

personaGPT

af1tang

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personaGPT is a conversational agent designed to generate personalized responses based on input personality facts, and to incorporate turn-level goals into its responses through "action codes". It builds on the DialoGPT-medium pretrained model, which is based on the GPT-2 architecture. personaGPT was trained on the Persona-Chat dataset, with added special tokens to distinguish between conversational history and personality traits for dyadic conversations. The model also used active learning to train it to do controlled decoding using turn-level goals. Model inputs and outputs personaGPT takes in a conversation history and personality facts as input, and generates the next response in the conversation. The model is designed to produce responses that are tailored to the user's personality and the current state of the conversation. Inputs Conversation history**: The prior messages exchanged in the conversation. Personality facts**: Information about the user's personality, such as their interests, background, and traits. Outputs Personalized response**: The model's generated response, which takes into account the user's personality and the current state of the conversation. Capabilities personaGPT is able to generate coherent and relevant responses that are tailored to the user's personality and the current state of the conversation. This can be useful for creating more engaging and personalized conversational experiences. The model's ability to incorporate turn-level goals can also allow for more purposeful and goal-oriented dialogues. What can I use it for? personaGPT could be used to develop chatbots or virtual assistants that can engage in more natural and personalized conversations. This could be useful in a variety of contexts, such as customer service, education, or entertainment. The model's capabilities could also be leveraged to create more interactive and immersive storytelling experiences. Things to try One interesting thing to try with personaGPT is to experiment with different personality profiles and see how the model's responses change. You could also try incorporating different turn-level goals, such as "talk about work" or "ask about favorite music", and observe how the model's responses adapt to these objectives. Additionally, you could explore how well the model performs on open-ended conversations, where the topic and direction of the dialogue is not predetermined.

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