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

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