Text simplification, crucial in natural language processing, aims to make
texts more comprehensible, particularly for specific groups like visually
impaired Spanish speakers, a less-represented language in this field. In
Spanish, there are few datasets that can be used to create text simplification
systems. Our research has the primary objective to develop a Spanish financial
text simplification dataset. We created a dataset with 5,314 complex and
simplified sentence pairs using established simplification rules. We also
compared our dataset with the simplifications generated from GPT-3, Tuner, and
MT5, in order to evaluate the feasibility of data augmentation using these
systems. In this manuscript we present the characteristics of our dataset and
the findings of the comparisons with other systems. The dataset is available at
Hugging face, saul1917/FEINA.

本研究的主要目标是开发一个西班牙语金融文本简化数据集，并通过使用 GPT-3、Tuner 和 MT5 等系统生成的简化进行比较，评估数据增强的可行性。

西班牙金融教育文本简化的新数据集

A Novel Dataset for Financial Education Text Simplification in Spanish

We present a new financial domain large language model, InvestLM, tuned on
LLaMA-65B (Touvron et al., 2023), using a carefully curated instruction dataset
related to financial investment. Inspired by less-is-more-for-alignment (Zhou
et al., 2023), we manually curate a small yet diverse instruction dataset,
covering a wide range of financial related topics, from Chartered Financial
Analyst (CFA) exam questions to SEC filings to Stackexchange quantitative
finance discussions. InvestLM shows strong capabilities in understanding
financial text and provides helpful responses to investment related questions.
Financial experts, including hedge fund managers and research analysts, rate
InvestLM's response as comparable to those of state-of-the-art commercial
models (GPT-3.5, GPT-4 and Claude-2). Zero-shot evaluation on a set of
financial NLP benchmarks demonstrates strong generalizability. From a research
perspective, this work suggests that a high-quality domain specific LLM can be
tuned using a small set of carefully curated instructions on a well-trained
foundation model, which is consistent with the Superficial Alignment Hypothesis
(Zhou et al., 2023). From a practical perspective, this work develops a
state-of-the-art financial domain LLM with superior capability in understanding
financial texts and providing helpful investment advice, potentially enhancing
the work efficiency of financial professionals. We release the model parameters
to the research community.

我们提出了一个新的金融领域大型语言模型 InvestLM，在 LLaMA-65B 上进行了调优，使用了一个与金融投资相关的精心策划的指令数据集。InvestLM 在理解金融文本方面表现出强大的能力，并对投资相关问题提供有价值的回答。这项工作开发了一个最先进的金融领域语言模型，具有优越的理解金融文本和提供有益投资建议的能力，可能提高金融专业人员的工作效率。