Summary of Mtfineval:a Multi-domain Chinese Financial Benchmark with Eurypalynous Questions, by Xinyu Liu and Ke Jin
MTFinEval:A Multi-domain Chinese Financial Benchmark with Eurypalynous questions
by Xinyu Liu, Ke Jin
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a new benchmark, MTFinEval, designed to measure the theoretical level and generalization ability of large language models (LLMs) in economics. The existing benchmarks are inadequate as they focus on specific application scenarios and use outdated datasets that don’t reflect real-world problems. MTFinEval consists of 360 questions refined from six major disciplines of economics, aiming to comprehensively assess LLMs’ capabilities. The experiment results show that all LLMs perform poorly on MTFinEval, demonstrating the success of this benchmark in evaluating their basic knowledge. This research provides guidance for selecting suitable LLMs for specific use cases and increases the rigor and reliability of these models from a theoretical perspective. Additionally, it highlights the importance of considering the limitations of current benchmarks and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well large language models (LLMs) understand economics. Right now, we’re using old benchmarks that only look at specific situations, like making decisions about investments or analyzing financial reports. But these tests don’t really show if the LLMs can think deeply and apply what they know in different contexts. To fix this, the researchers built a new benchmark called MTFinEval, which includes questions from university textbooks and exam papers that cover six main areas of economics. The results show that all the LLMs struggled on this test, proving that it’s a good way to measure their basic knowledge. This can help us choose the right LLM for specific jobs and make sure they’re reliable. |
Keywords
» Artificial intelligence » Generalization
