OpenFinLLM Leaderboard Documentation

Introduction

  • Overview
  • Model List
  • Demo

Financial Question Trees

  • Overview
  • BloombergGPT Datasets
  • Information Extraction (IE)
  • Textual Analysis (TA)
  • Question Answering (QA)
  • Text Generation (TG)
  • Risk Management (RM)
  • Forecasting (FO)
  • Decision-Making (DM)
  • Other Datasets

Real-world Use Cases

  • Simple Questions
  • Efficient Legal Consultation Prep
    • Overview
    • Lawyer Consultation Cost Comparison
    • Example of Question Refinement
    • Back-and-Forth QA Analysis
      • Key Inefficiencies
      • LLM Benefits
  • Financial Document Analysis
  • AI-Driven Regulatory Compliance in Capital Markets
  • XBRL Analytics

Finagents

  • Tutor Agent
  • Search Agent
  • Trading Agent
  • XBRL Agent
  • ECC Analyzer

References

  • References

Tutorials

  • FPB Dataset Evaluation with O3-Mini
  • Benchmark DeepSeek on Financial Sentiment Analysis
  • Benchmark DeepSeek on Financial Questions with RAG
  • Benchmark DeepSeek on Financial Sentiment Analysis (few-shot)
  • Benchmark Llama-3.2 on Financial Sentiment Analysis (zeroshot)
  • Benchmark GPT-4o on Financial Sentiment Analysis (zeroshot)
  • Evaluate Deepseek using Framework

Experiment Settings

  • Install Cuda
  • Obtaining DeepSeek API Key
  • Get Model API keys

Industry Collaboration

  • FAQs
  • Contributions
OpenFinLLM Leaderboard Documentation
  • Efficient Legal Consultation Prep
  • Edit on GitHub

Efficient Legal Consultation Prep

Table of Contents

  • Overview

  • Lawyer Consultation Cost Comparison

  • Example of Question Refinement

  • Back-and-Forth QA Analysis

    • Key Inefficiencies

    • LLM Benefits

Overview

Illustrates how using an LLM to refine questions before legal consultations can improve communication efficiency with lawyers, saving time and reducing costs.

Lawyer Consultation Cost Comparison

Lawyer consultation cost comparison diagram

Comparison of consultation costs with/without LLM question refinement

Key points:

  • Traditional workflow involves repetitive Q&A leading to higher fees

  • LLM-prepared questions enable focused discussions

  • Potential time reduction from hours to fractions

  • Significant cost savings through efficiency gains

Example of Question Refinement

Question refinement example

Fig. 6 LLM-generated compliance questions for investment fund usage

Use case scenario:

The LLM assists by generating a list of specific, refined questions to present to the lawyer, reducing the consultation time and ensuring that key contract compliance areas are covered.

Back-and-Forth QA Analysis

Inefficient QA example

Fig. 7 Example of costly repetitive questioning with lawyers

Key Inefficiencies

  • Repetitive Questions: Similar queries about fund misuse

  • Basic Clarifications: Fundamental term explanations

  • Unrealistic Proposals: Over-simplified compliance ideas

  • High Costs: Lawyer time charges accumulate quickly

LLM Benefits

  • Cost Reduction: Free handling of basic queries

  • Preparation: Structured question organization

  • 24/7 Availability: Instant response capability

  • Expertise Bridging: Compensates for legal knowledge gaps

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