FunSearch Method to Improve Large Language Models
Large Language Models (LLMs) are designed to mimic human language, improving communication between machines and humans. They are versatile and adaptable across various tasks and industries, including language translation, summarization, and question answering.
Challenges with LLMs
While LLMs are highly advanced, they sometimes produce incorrect statements due to hallucinations. This can occur when the input or prompt provided to the model is ambiguous, contradictory, or misleading.
The FunSearch Method
Researchers at Google DeepMind have developed a method called FunSearch to address these limitations. FunSearch combines a pre-trained LLM with an evaluator to guard against confabulations and incorrect ideas. It operates as an iterative process, where promising programs are reintroduced into the pool of existing programs to establish a self-enhancing loop.
FunSearch uses an island-based evolutionary method to maintain a large pool of diverse programs, allowing for real-world applications.
The Future of FunSearch
FunSearch has the potential for real-world applications and align with the broader evolution of LLMs. Researchers are committed to expanding its functionalities to solve critical scientific and engineering challenges prevalent in society.