Summary of Evaluating Large Language Models on Financial Report Summarization: An Empirical Study, by Xinqi Yang et al.
Evaluating Large Language Models on Financial Report Summarization: An Empirical Study
by Xinqi Yang, Scott Zang, Yong Ren, Dingjie Peng, Zheng Wen
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Medium Difficulty Summary: Recent advances in Large Language Models (LLMs) have led to remarkable versatility across various applications. However, applying LLMs to high-stakes domains like finance requires rigorous evaluation to ensure reliability, accuracy, and compliance with industry standards. Our study compares three state-of-the-art LLMs – GLM-4, Mistral-NeMo, and LLaMA3.1 – in generating automated financial reports. We explore how these models can be harnessed within finance, a field demanding precision, contextual relevance, and robustness against erroneous information. Our paper provides benchmarks for financial report analysis, using metrics such as ROUGE-1, BERT Score, and LLM Score. We introduce an innovative evaluation framework that integrates quantitative and qualitative analyses to assess each model’s output quality. Additionally, we make our financial dataset publicly available, inviting researchers and practitioners to leverage, scrutinize, and enhance our findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Scientists have been developing special computer models called Large Language Models (LLMs) that can understand and generate text. These models are very good at doing things like understanding what people mean when they write sentences. But the question is: can these models be trusted to make important decisions, like in finance? We tested three of these LLMs on a big task – generating reports about financial data. We wanted to see how well they did and if we could trust their results. We came up with some special ways to measure how good each model was at doing this job. And we made all the data we used public, so other people can look at it and help us make our findings even better. |
Keywords
» Artificial intelligence » Bert » Precision » Rouge
