WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine

TL;DR

WeaverBird is an AI-powered finance dialogue system that combines a fine-tuned GPT model, a knowledge base, and a search engine to provide accurate, credible financial advice with source citations.

Contribution

This work introduces WeaverBird, a novel finance-specific dialogue system integrating large language models, knowledge retrieval, and search capabilities for improved financial query responses.

Findings

Outperforms existing models on finance-related questions

Provides responses with source citations for credibility

Demonstrates effective integration of LLM, knowledge base, and search engine

Abstract

We present WeaverBird, an intelligent dialogue system designed specifically for the finance domain. Our system harnesses a large language model of GPT architecture that has been tuned using extensive corpora of finance-related text. As a result, our system possesses the capability to understand complex financial queries, such as "How should I manage my investments during inflation?", and provide informed responses. Furthermore, our system incorporates a local knowledge base and a search engine to retrieve relevant information. The final responses are conditioned on the search results and include proper citations to the sources, thus enjoying an enhanced credibility. Through a range of finance-related questions, we have demonstrated the superior performance of our system compared to other models. To experience our system firsthand, users can interact with our live demo at https://weaverbird.ttic.edu, as well as watch our 2-min video illustration at https://www.youtube.com/watch?v=yofgeqnlrMc.