Semantic Scholar Open Access 2023 6 sitasi

When to Use Large Language Model: Upper Bound Analysis of BM25 Algorithms in Reading Comprehension Task

Tingzhen Liu Qianqian Xiong Shengxi Zhang

Lihat Sumber DOI

Abstrak

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-turning 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.

Penulis (3)

Tingzhen Liu

Qianqian Xiong

Shengxi Zhang

Format Sitasi

APA MLA BibTeX

Liu, T., Xiong, Q., Zhang, S. (2023). When to Use Large Language Model: Upper Bound Analysis of BM25 Algorithms in Reading Comprehension Task. https://doi.org/10.1109/icnlp58431.2023.00049

Akses Cepat

Lihat di Sumber doi.org/10.1109/icnlp58431.2023.00049

Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Total Sitasi: 6×
Sumber Database: Semantic Scholar
DOI: 10.1109/icnlp58431.2023.00049
Akses: Open Access ✓