InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning
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Abstract (arXiv)
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.
Key facts. Cited by 80 papers in the map (data through 7 Oct 2026). Builds on 21 of its 29 references that are in the map. First posted on arXiv 15 Sep 2023.
Builds on
21 of 29 references.
References · 21
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- 2023PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Financecited by 217
- 2020FinBERT: A Pretrained Language Model for Financial Communicationscited by 161
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- 2022ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcriptscited by 59
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Citing papers per year: 2023: 5; 2024: 30; 2025: 34; 2026: 11.
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