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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.10921 |
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| _version_ | 1866929466121388032 |
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| author | Liu, Xinyu Jin, Ke |
| author_facet | Liu, Xinyu Jin, Ke |
| contents | With the emergence of more and more economy-specific LLMS, how to measure whether they can be safely invested in production becomes a problem. Previous research has primarily focused on evaluating the performance of LLMs within specific application scenarios. However, these benchmarks cannot reflect the theoretical level and generalization ability, and the backward datasets are increasingly unsuitable for problems in real scenarios. In this paper, we have compiled a new benchmark, MTFinEval, focusing on the LLMs' basic knowledge of economics, which can always be used as a basis for judgment. To examine only theoretical knowledge as much as possible, MTFinEval is build with foundational questions from university textbooks,and exam papers in economics and management major. Aware of the overall performance of LLMs do not depend solely on one subdiscipline of economics, MTFinEval comprise 360 questions refined from six major disciplines of economics, and reflect capabilities more comprehensively. Experiment result shows all LLMs perform poorly on MTFinEval, which proves that our benchmark built on basic knowledge is very successful. Our research not only offers guidance for selecting the appropriate LLM for specific use cases, but also put forward increase the rigor reliability of LLMs from the basics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10921 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MTFinEval:A Multi-domain Chinese Financial Benchmark with Eurypalynous questions Liu, Xinyu Jin, Ke Artificial Intelligence With the emergence of more and more economy-specific LLMS, how to measure whether they can be safely invested in production becomes a problem. Previous research has primarily focused on evaluating the performance of LLMs within specific application scenarios. However, these benchmarks cannot reflect the theoretical level and generalization ability, and the backward datasets are increasingly unsuitable for problems in real scenarios. In this paper, we have compiled a new benchmark, MTFinEval, focusing on the LLMs' basic knowledge of economics, which can always be used as a basis for judgment. To examine only theoretical knowledge as much as possible, MTFinEval is build with foundational questions from university textbooks,and exam papers in economics and management major. Aware of the overall performance of LLMs do not depend solely on one subdiscipline of economics, MTFinEval comprise 360 questions refined from six major disciplines of economics, and reflect capabilities more comprehensively. Experiment result shows all LLMs perform poorly on MTFinEval, which proves that our benchmark built on basic knowledge is very successful. Our research not only offers guidance for selecting the appropriate LLM for specific use cases, but also put forward increase the rigor reliability of LLMs from the basics. |
| title | MTFinEval:A Multi-domain Chinese Financial Benchmark with Eurypalynous questions |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2408.10921 |