β Large language models can be used as optimizers for exploration and finding optimal or near-optimal solutions in different optimization problems.
π The paper discusses three examples: linear regression, the traveling salesman problem, and optimizing prompts for other tasks using a large language model.
β The authors address the question of why use large language models for optimization problems that already have well-defined solutions.
π Large language models (LLMs) can be used for optimization problems by using natural language prompts.
βοΈ LLMs can solve optimization problems that are not well-formulated, such as traveling salesman or linear regression, using natural language prompts.
ποΈ The solution involves a meta prompt that describes the optimization problem and contains example solutions, which are iteratively refined by the LLM until an optimal score is achieved.
π Large language models can optimize for values of w and b in linear regression.
π The process involves generating new pairs and minimizing the function value.
πΊοΈ The same approach can be used for solving traveling salesman problems.
π‘ Large language models can be used as optimizers to find optimal solutions to problems.
π GPT-4 can find the optimum solution or a solution close to it for traveling salesmen problems with up to 20 points on the plane.
π LLMs can be used to find good prompts for GSM 8K and Big Bench Hard tasks, which are benchmarks for reasoning with LLMs.
π‘ The challenge is finding a prompt that performs best in solving two benchmarks.
π The authors treat the act of finding a good prompt as an optimization problem.
π A meta prompt is constructed to generate new prompts and achieve higher success scores.
π The video discusses the use of large language models as optimizers.
π§ A method called prompt optimization is used to create new prompts that achieve better performance than default prompts.
π‘ Instead of relying on human prompt engineers, the large language model itself is asked to generate better prompts.
π Language models are highly effective in reasoning and arithmetic tasks.
π These models perform well in various benchmarks.
π Consider supporting the creator by subscribing and liking the video.
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