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。

InvestLM：一种用于投资的大规模语言模型，利用金融领域指令调整

InvestLM: A Large Language Model for Investment using Financial Domain  Instruction Tuning

Personalisation of products and services is fast becoming the driver of
success in banking and commerce. Machine learning holds the promise of gaining
a deeper understanding of and tailoring to customers' needs and preferences.
Whereas traditional solutions to financial decision problems frequently rely on
model assumptions, reinforcement learning is able to exploit large amounts of
data to improve customer modelling and decision-making in complex financial
environments with fewer assumptions. Model explainability and interpretability
present challenges from a regulatory perspective which demands transparency for
acceptance; they also offer the opportunity for improved insight into and
understanding of customers. Post-hoc approaches are typically used for
explaining pretrained reinforcement learning models. Based on our previous
modeling of customer spending behaviour, we adapt our recent reinforcement
learning algorithm that intrinsically characterizes desirable behaviours and we
transition to the problem of asset management. We train inherently
interpretable reinforcement learning agents to give investment advice that is
aligned with prototype financial personality traits which are combined to make
a final recommendation. We observe that the trained agents' advice adheres to
their intended characteristics, they learn the value of compound growth, and,
without any explicit reference, the notion of risk as well as improved policy
convergence.

通过机器学习和强化学习算法，引入可解释性的特征，应用于个性化银行和金融服务，实现资产管理和投资建议的个性化推荐。