VerifAI introduces the MultiLLM, a Python framework open to all, that empowers users to harness the collective might of multiple Language Model Models (LLMs) concurrently. By orchestrating these multiple LLMs in parallel and evaluating their outputs, the MultiLLM from VerifAI sets its sights on identifying the most precise results, often referred to as the “ground truth.”

Initially, the MultiLLM is tailored to a specific application: the comparison of code generated by well-known LLMs, including GPT3, GPT5, and Google-Bard. Nevertheless, this adaptable framework extends its support to embrace fresh LLMs and provides the flexibility to fine-tune ranking functions, ensuring the scrutiny of a wide array of responses from diverse LLMs.

With its adaptable and versatile design, VerifAI’s MultiLLM offers users the indispensable capability to obtain dependable results for various tasks. Whether it’s soliciting code or seeking answers to specific queries, MultiLLM capitalizes on the combined knowledge of multiple LLMs, comparing their outputs to deliver the most reliable and optimal solutions.

It’s crucial to note that individual LLMs may, on occasion, present inaccuracies concerning people, places, or factual information. Therefore, by amalgamating outputs from multiple LLMs and cross-referencing their results through the VerifAI MultiLLM framework, users can effectively mitigate the risks associated with relying solely on potentially erroneous data.

For those eager to delve deeper, the MultiLLM framework is accessible as an open-source resource on GitHub, with additional insights available in the associated VerifAI blog article.