InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning
HKUST IEMS Thought Leadership Brief No. 87
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We present InvestLM, a state-of-the-art Large Language Model for the financial domain, tuned on the LLaMA65B foundation model, using a set of manually crafted instruction datasets covering diverse financial and investment related topics. InvestLM shows strong capabilities in understanding financial text and offers helpful insights in response to investment-related inquiries comparable to GPT-3.5, GPT-4, and Claude-2. Applying a diverse set of high-quality, domain-specific instructions to train an LLM is more effective in enhancing its capabilities for handling domain-specific tasks than using a large volume of general-purpose instructions.
About the Author
Yi Yang is an Associate professor in the Department of Information Systems, Business Statistics and Operations Management at Hong Kong University of Science and Technology. He is the Director of the Center for Business and Social Analytics (CBSA). He received his PhD in computer science from Northwestern University. He designs machine learning methods in his research to solve challenging business and Fintech problems. His work has been published in business discipline journals such as Information Systems Research, Management Information Systems Quarterly, Journal of Marketing, Contemporary Accounting Research and INFORMS Journal on Computing. His work has also been published in top-tier machine learning and natural language processing conferences such as Annual Meeting of the Association for Computational Linguistics (ACL), Conference on Empirical Methods in Natural Language Processing (EMNLP) and International Conference on Artificial Intelligence and Statistics (AISTATS).
Yixuan Tang is an MPhil student in Information Systems at the Hong Kong University of Science and Technology. Yixuan has cultivated her interest in Natural language processing (NLP) in Finance. She is particularly passionate about adapting Large Language Models in the Finance domain and mining finance signals from text embedding. She has published in machine learning conferences such as the Conference on Empirical Methods in Natural Language Processing (EMNLP) and the Conference on Language Modeling (COLM).
Kar Yan Tam is Vice-President for Administration and Business and Chair Professor of Information Systems, Business Statistics and Operations Management at the Hong Kong University of Science and Technology (HKUST). Prof Tam is known for his contributions in information systems and the diffusion of innovations in organizations. According to Google Scholar, his publications have received over 23,000 citations. Prof Tam is currently serving on the editorial board of a number of academic journals. Prof Tam also plays an active role in public services. He is a member of the Hong Kong Exchange Fund Advisory Committee of the Hong Kong Monetary Authority and the Chairperson of the Hong Kong Committee for the Pacific Economic Collaboration.
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