fingpt/FinGPT_MultiAgentsRAG/README.md

Financial Language Models for Reducing Hallucinations 💡

Introduction 🚀

Welcome to our project repository, where we aim to address the challenge of fact-conflict hallucinations in Large Language Models (LLMs) with a focus on the financial domain. Our approach integrates innovative techniques like Multi-Agent Systems (MAS) and Retrieval-Augmented Generation (RAG) to enhance the factuality of LLM outputs.

Project Overview 📊

• Hallucination Mitigation: Tackling financial fact-conflict hallucinations with our novel framework.
• MAS Debates: Implementing a debate framework within MAS to improve reasoning and accuracy.
• RAG: Leveraging up-to-date external knowledge to inform and refine the language model responses.
• Financial Expertise: Fine-tuning our models with rich financial datasets for domain-specific expertise.

Dataset 📁

We utilize diverse financial datasets including FiQA and WealthAlpaca, equipping our models with robust financial knowledge.

Methodology 🛠️

1. Instruction-Tuning: Leveraging instruction-tuning to enhance the financial acumen of our models.
2. MAS Integration: Orchestrating debates among agents to critique and refine responses.
3. RAG Workflow: Incorporating a custom retrieval engine to supplement model responses with external knowledge.