ARTICLE | doi:10.20944/preprints202301.0219.v2
Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: Large Language Model; natural language processing; reading comprehension; computational lin-guistics; information retrieval; BM25
Online: 30 March 2023 (03:51:37 CEST)
Large language model (LLM) is a representation of a major advancement in AI, and has been used in multiple natural language processing tasks. Nevertheless, in different business scenarios, LLM requires fine-tuning by engineers to achieve satisfactory performance, and the cost of achieving target performance and fine-tuning may not match. Based on the Baidu STI dataset, we study the upper bound of the performance that classical information retrieval methods can achieve under a specific business, and compare it with the cost and performance of the participating team based on LLM. This paper gives an insight into the potential of classical computational linguistics algorithms, and which can help decision-makers make reasonable choices for LLM and low-cost methods in business R&D.