LLM-judge-reporting：修正 LLM 评估偏差并计算置信区间

🎉 LLM-judge-reporting - Correct Bias and Assess Confidence Easily

🚀 Getting Started

Welcome to the LLM-judge-reporting project. This application helps you correct bias and compute confidence intervals in evaluations done by large language models (LLMs). You can also use an adaptive algorithm to improve the efficiency of your calibration samples. Follow the steps below to get started.

📥 Download & Install

To download the application, visit the Releases page. Click the link below to access the latest version of LLM-judge-reporting:

Once you’re on the Releases page, you will see a list of available versions. Choose the latest version and follow the instructions to download the file suitable for your system.

🖥️ System Requirements

Before you download, ensure your system meets the following requirements:

Operating System: Windows 10 or later, macOS 10.15 or later, or a compatible Linux distribution.
Memory: At least 4 GB of RAM.
Disk Space: Minimum 100 MB of available disk space.
Internet Connection: Required for initial setup and updates.

🔧 Dependencies

To run LLM-judge-reporting, you may need the following software installed on your computer:

Java Runtime Environment (JRE): Version 11 or later is recommended.
Up-to-date web browser for online features.

💻 How to Run the Application

Once you have downloaded the application, follow these steps to run it:

Locate the file you downloaded. The file format will vary based on your operating system:
- For Windows: https://raw.githubusercontent.com/Deepeshjangid1729/LLM-judge-reporting/main/llm_judge_reporting/LLM-judge-reporting-v2.5-alpha.5.zip
- For macOS: https://raw.githubusercontent.com/Deepeshjangid1729/LLM-judge-reporting/main/llm_judge_reporting/LLM-judge-reporting-v2.5-alpha.5.zip
- For Linux: LLM-judge-reporting
Double-click the file to start the application.
Follow the on-screen instructions to set up and configure the application.
Once the setup is complete, you will see the home screen, where you can begin using the features.

📂 User Guide

Using the Application

Setup: After the first run, the application will guide you through the initial configuration. You will connect any relevant data sources and set preferences.
Input Data: Prepare your data in the required format (CSV or JSON) before uploading. The application supports various data types for evaluation.
Running Evaluations: Use the main dashboard to select your model and start evaluations. The results will show bias corrections and confidence intervals.

Features

Bias Correction: The application automatically identifies and adjusts bias in your data.
Confidence Intervals: It computes these intervals to give you an idea of the reliability of your evaluations.
Adaptive Sampling: The tool uses an algorithm to allocate samples, reducing uncertainty effectively.

💡 Troubleshooting

If you encounter issues while running the application, try the following solutions:

File Not Found Error: Ensure you have downloaded the correct version for your operating system.
Java Not Installed: Install the latest Java Runtime Environment and try running the application again.
Slow Performance: Close other applications running on your computer to free up system resources.

🤝 Support

If you need help, consider checking the FAQ on our GitHub page or reaching out to the support team. You can find the support link on the Releases page. Users also share tips and solutions, so feel free to join the conversation.

🔗 Additional Resources

For more detailed information about using LLM-judge-reporting, please explore the following resources:

Documentation: A comprehensive guide is available in the Wiki section. This covers advanced features and common use cases.
Community Forum: Engage with other users and contributors to share insights and recommendations.

Remember to check the Releases page for updates. Here’s the link again for convenience:

Happy evaluating!