Construction of a Japanese Financial Benchmark for Large Language Models
This provides a domain-specific evaluation tool for Japanese financial applications, but it is incremental as it adapts existing benchmark concepts to a new language and domain.
The authors constructed a Japanese financial benchmark for evaluating large language models, confirming that GPT-4 performs outstandingly and that the benchmark effectively differentiates model scores across performance ranges.
With the recent development of large language models (LLMs), models that focus on certain domains and languages have been discussed for their necessity. There is also a growing need for benchmarks to evaluate the performance of current LLMs in each domain. Therefore, in this study, we constructed a benchmark comprising multiple tasks specific to the Japanese and financial domains and performed benchmark measurements on some models. Consequently, we confirmed that GPT-4 is currently outstanding, and that the constructed benchmarks function effectively. According to our analysis, our benchmark can differentiate benchmark scores among models in all performance ranges by combining tasks with different difficulties.