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Coding Reliable LLM-based Integrated Task and Knowledge Agents with GenieWorksheets by Joshi, Harshit; Liu, Shicheng; Chen, James; Weigle, Robert; Lam, Monica S. is a scholarly article available to read on EtoBox.

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Large Language Models (LLMs) present an opportunity to create automated assistants that can help users navigate complex tasks. However, existing approaches have limitations in handling conditional logic, integrating knowledge sources, and consistently following instructions. Researchers and industry professionals often employ ad hoc pipelines to construct conversational agents. These pipelines aim to maintain context, address failure cases, and minimize hallucinations, yet frequently fail to achieve these objectives. To this end, we present Genie - a programmable framework for creating task-oriented conversational agents that are designed to handle complex user interactions and knowledge queries. Unlike LLMs, Genie provides reliable grounded responses, with controllable agent policies through its expressive specification, Genie Worksheet. In contrast to dialog trees, it is resilient to diverse user queries, helpful with knowledge sources, and offers ease of programming policies through its declarative paradigm. The agents built using Genie outperforms the state-of-the-art method on complex logic domains in STARV2 dataset by up to 20.5%. Additionally, through a real-user study invol

Author

Joshi, Harshit; Liu, Shicheng; Chen, James; Weigle, Robert; Lam, Monica S.

Published

2024

Language

EN