Some LLMs may produce inaccurate responses due to hallucinations. Google DeepMind researchers propose FunSearch, a method to address this issue. It combines a pre-trained LLM with an evaluator to discover new knowledge by evolving low-scoring programs into high-scoring ones. This iterative process has significant potential for real-world applications and aims to expand functionalities to tackle scientific and engineering challenges.
Understanding Large Language Models (LLMs) and FunSearch
Improving Communication Between Machines and Humans
Large Language Models (LLMs) are adept at understanding and generating human-like text, enabling better communication between machines and humans. They can handle tasks such as language translation, summarization, question answering, text generation, and sentiment analysis across various industries.
Challenges and Solutions
However, LLMs can sometimes produce incorrect responses due to hallucinations, especially when the input is ambiguous or misleading. To address this, Google DeepMind has developed FunSearch, a method that combines a pre-trained LLM with an evaluator to guard against confabulations and incorrect ideas.
How FunSearch Works
FunSearch operates as an iterative process, picking certain programs from a pool and processing them with an LLM to produce fresh programs that undergo automatic evaluation. The most promising ones are reintroduced into the pool, creating a self-enhancing loop. Researchers then sample the better-performing programs and input them back into LLMs as prompts to improve them.
Real-World Applications
FunSearch operates in the space of programs, giving it the potential for real-world applications, particularly in mathematics and computer science.
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