An overview of using large language models for the symbol grounding task in ABC repair system
Year: 2024
Type: conference-paper
Abstract: The ABC Theory Repair System (ABC) has demonstrated its success in facilitating users to repair faulty theories utilizing distinct techniques. Yet, comprehending ABC-repaired theories becomes more challenging due to the presence of dummy constants or predicates introduced by ABC. In this paper, we propose a grounding system by incorporating Large Language Models (LLMs) to provide these dummy items with meaningful names. By applying ABC and grounding alternately, the resulting theory is both fault-free and semantically meaningful. Moreover, our study shows that LLMs without fine-tuning still exhibit capabilities of common knowledge, and their grounding performances are enhanced by providing sufficient background or asking for more returns.
Authors: Chan, Pak Yin, Xue Li, Bundy, Alan
Institution: University of Edinburgh
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7115216702