GODEL-v1_1-large-seq2seq
| Property | Value |
|---|---|
| Author | Microsoft |
| License | MIT |
| Paper | arXiv:2206.11309 |
| Training Data | 551M multi-turn dialogs + 5M instruction dialogs |
What is GODEL-v1_1-large-seq2seq?
GODEL (Goal-Directed Dialog Enhanced Large-scale model) is a sophisticated conversational AI model developed by Microsoft. It's built on a Transformer-based encoder-decoder architecture and specifically designed for goal-directed dialogues with external knowledge grounding capabilities.
Implementation Details
The model leverages a sequence-to-sequence architecture implemented using PyTorch and the T5 framework. It's been trained on an extensive dataset of 551M multi-turn dialogs from Reddit discussions and 5M instruction and knowledge-grounded dialogs.
- Transformer-based encoder-decoder architecture
- Supports both open-ended conversation and knowledge-grounded responses
- Implements efficient fine-tuning capabilities for task-specific adaptations
Core Capabilities
- Empathetic response generation in multi-turn conversations
- Knowledge-grounded response generation
- Context-aware dialogue management
- Flexible instruction following for different dialogue tasks
Frequently Asked Questions
Q: What makes this model unique?
GODEL's distinctive feature is its ability to generate responses grounded in external text while maintaining natural conversation flow. This makes it particularly effective for tasks requiring both factual accuracy and conversational fluency.
Q: What are the recommended use cases?
The model excels in scenarios requiring empathetic responses, knowledge-based conversations, and goal-directed dialogue tasks. It's particularly suitable for chatbots requiring both factual grounding and natural conversation abilities.
