We just released the Agentic FinSearch, which is currently a Google extension. It is a user-friendly agent, a potential replacement of Bloomberg Terminal

Is it like a personal assistant that guides an individual through existing practical tools?

Any thoughts about the (financial) data sources, to avoid garbage input?

Thanks!

Thanks for sharing the links!! Appreciate it.

This is the latest paper on GPTQuant based on LangChain:

GPTQuant's Conversational AI: Simplifying Investment Research for All:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4380516

A few points to share:

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.)

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.)

We are aiming for feeding Internet-scale financial data into LLMs, and adapt LLMs to the finance sector. Happy to know your thoughts!