1). Finance is high dynamic. BloombergGPT retrains LLM using a mixed dataset of finance and general sources is too much expensive (1.3M hours). Lightweight adaptation is highly favorable.
2). Internet-scale finance data (timely updates using an automatic data curation pipeline) is critical. BloombergGPT has privileged data access and API access. A promising alternative is "democratizing Internet-scale finance data".
3). Another key technology is "RLHF (Reinforcement learning from human feedback)", which is missing in BloombergGPT. RLHF enables learning individual preferences (risk-aversion level, investing habits, personalized robo-advisor, etc.)
2). Internet-scale finance data (timely updates using an automatic data curation pipeline) is critical. BloombergGPT has privileged data access and API access. A promising alternative is "democratizing Internet-scale finance data".
3). Another key technology is "RLHF (Reinforcement learning from human feedback)", which is missing in BloombergGPT. RLHF enables learning individual preferences (risk-aversion level, investing habits, personalized robo-advisor, etc.)