Year: 2024
Type: conference-paper
Abstract: This paper addresses the underexplored intersection of prompt engineering techniques and system design in the context of small Large Language Models (LLMs) aimed at generating structured outputs. While prior studies have largely focused on larger LLMs, the potential of smaller models, which operate with significantly fewer parameters, remains largely untapped, particularly concerning entity extraction in real world task-oriented dialogues. We propose a framework for deploying small LLMs in private, resource-efficient environments to enhance task-oriented workflows. Utilizing the TaskMaster-1 (TM-1-2019) dataset, our research demonstrates how structured outputs can be generated effectively in the absence of any fine-tuning. We evaluate various small LLMs and analyze critical metrics such as validity to identify the key factors and components necessary for transforming dialogue into actionable entities within programming environments.
Authors: Sudhan, Adithya, Shahidi, Reza, Fiech, Adrian
Institution: Memorial University of Newfoundland
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7008123231
FWCI:
Cites:
Cited by:
Related to: