핵심 개념
OAT-v2 is an open-source conversational system that enables scalable and robust experimentation in various domains, supporting multimodal interactions and generative neural models.
초록
Abstract:
Introduces OAT-v2, an open-source task-oriented conversational system.
Highlights the system's modular components and capabilities for user interaction.
Introduction:
Discusses the need for open-source conversational frameworks.
Introduces the first version of OAT and its purpose during the Alexa Prize TaskBot Challenge.
OAT-v2 Extension:
Describes the enhancements in OAT-v2 for generative neural models.
Explains the system's components like action code generation and response generation.
Data Extraction:
"OAT-v2 provides open models and software for research and commercial applications."
"OAT-v2 uses a modular setup using Docker and Kubernetes."
Offline Pipeline:
Details the process of parsing and augmenting task data from CommonCrawl.
Discusses the offline pipeline's role in enhancing tasks for real-world assistance.
Dockerised Modular Architecture:
Explains the modular format of OAT-v2 for version control and deployment.
Describes the Docker containers used for online, offline, and deployment purposes.
Online Infrastructure:
Reviews the additions made in OAT-v2 for version 2.
Discusses the NDP model for action code generation and LLM extension.
Code Generation for Dialogue Management:
Introduces the Neural Decision Parser (NDP) model in OAT-v2.
Explains the action space for NDP code generation.
LLM Generation using TGI:
Discusses the use of locally deployable LLMs in OAT-v2.
Explains the interaction with Huggingface's Text Generation Interface for model deployment.
Composed Response Generation:
Details the backend handling of NDP actions in OAT-v2.
Explains the system's response generation process for different action types.
Live Task Adaptation:
Highlights the importance of task adaptation in real-world scenarios.
Discusses the system's ability to modify tasks based on user preferences.
Synthetic Task Generation:
Explains the process of generating synthetic tasks in OAT-v2.
Discusses the training pipeline for the system's NDP.
Conclusion & Future Work:
Summarizes the key features and future vision of OAT-v2.
Discusses the potential for integrating multimodal LLMs and visual input in future work.
통계
"OAT-v2 provides open models and software for research and commercial applications."
"OAT-v2 uses a modular setup using Docker and Kubernetes."
인용구
"OAT-v2 provides open models and software for research and commercial applications."
"OAT-v2 is a proven system that enables scalable and robust experimentation in experimental and real-world deployment."