VerifAI's MultiLLM: Harnessing the Power of Multiple LLMs

VerifAI's MultiLLM is an open-source Python framework designed to enhance the accuracy and reliability of Large Language Model (LLM) outputs by leveraging the power of multiple LLMs simultaneously. It achieves this by running parallel queries across various LLMs and employing a ranking system to identify the most accurate result, effectively mitigating the risk of individual LLM inaccuracies.

Features

• Parallel LLM Invocation: Queries multiple LLMs concurrently for faster results.
• Output Ranking: A sophisticated ranking system identifies the most accurate response among different LLM outputs.
• Customizable Ranking Functions: Allows users to tailor the ranking process to specific needs and output types.
• Extensible LLM Support: Easily integrates new LLMs as they emerge.
• Open-Source and Accessible: Available on GitHub for community contribution and use.

Benefits

• Improved Accuracy: Reduces the risk of relying on potentially erroneous information from individual LLMs.
• Increased Reliability: Provides more consistent and trustworthy results across various tasks.
• Enhanced Performance: Faster response times through parallel processing.
• Flexibility and Customization: Adaptable to diverse tasks and user-defined ranking criteria.

Use Cases

• Code Generation Comparison: Currently focused on comparing code generated by LLMs like GPT3, GPT5, and Google Bard.
• Question Answering: Provides more accurate answers by consolidating responses from multiple LLMs.
• Fact Verification: Minimizes the risk of factual errors by cross-referencing information from various sources.

VerifAI's MultiLLM empowers users to obtain reliable and accurate results from LLMs by intelligently combining and ranking their outputs, making it a valuable tool for various applications.