Summary Leveraging Large Language Models for Optimization arxiv.org
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Leveraging Large Language Models (LLMs) for optimization through Optimization by PROmpting (OPRO) using natural language descriptions is possible, but LLMs have limitations including hallucinating values and generating ineffective solutions.
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Slide Presentation (10 slides)
Key Points
- Large Language Models (LLMs) can be leveraged for optimization using Optimization by PROmpting (OPRO).
- OPRO framework uses the full optimization trajectory to improve task accuracy.
- Optimization stability and exploration-exploitation trade-off are important considerations when using LLMs for optimization.
- Prompt optimization using LLMs shows notable performance gains in optimizing various tasks.
- Meta-prompt design and order of previous instructions affect the optimizer's output in prompt optimization.
Summaries
30 word summary
Leveraging Large Language Models (LLMs) for optimization is possible through Optimization by PROmpting (OPRO), using natural language descriptions. However, LLMs have limitations, such as hallucinating values and generating ineffective solutions.
78 word summary
Large language models (LLMs) can be used as optimizers through Optimization by PROmpting (OPRO), where the optimization task is described in natural language. The OPRO framework leverages LLMs to generate new prompts and improve task
This summary provides a list of references to research papers and articles related to optimization and language models. It discusses the limitations of Large Language Models (LLMs) in solving complex problems, such as hallucinating values in math calculations and generating ineffective solutions. To
989 word summary
Large language models (LLMs) can be leveraged as optimizers using Optimization by PROmpting (OPRO), where the optimization task is described in natural language. The LLM generates new solutions from the prompt containing previously generated solutions, which
The OPRO framework leverages Large Language Models (LLMs) for optimization by generating new prompts to increase test accuracy. It uses the full optimization trajectory to gradually improve task accuracy. The framework is evaluated on several LLMs and achieves notable performance gains
Optimization stability and exploration-exploitation trade-off are important considerations when using Large Language Models (LLMs) for optimization. LLMs can be sensitive to low-quality solutions in the input trajectory, resulting in instability and large variance. To improve stability
We evaluate the performance of large language models (LLMs) in optimizing the Traveling Salesman Problem (TSP) by comparing them to heuristics. We use Gurobi solver to construct oracle solutions and compute the optimality gap. Among
The excerpt discusses the application of text prompts and prompt optimization using large language models (LLMs). It provides an example of how to apply text prompts and demonstrates the effectiveness of prompt optimization in maximizing task accuracy. The problem setup for prompt optimization is explained,
In each optimization step, training examples are added to the meta-prompt, either through random sampling or by selecting ones that the previous instructions did not cover. The optimization trajectory includes past instructions and their scores, sorted by score in ascending order. Meta-in
The results of generating Q-begin instructions with the text-bison scorer and PaLM 2-L-IT optimizer are presented. The optimization curve shows an upward trend, with significant leaps in training accuracy occurring at various steps. The highest test accuracies for
Prompt optimization using large language models (LLMs) was performed on the GSM8K and BBH datasets. Different optimizer LLMs produced varying styles of instructions, with some being concise and others being long and detailed. The optimized instructions outperformed
The meta-prompt design is important for prompt optimization performance. The order of previous instructions affects the optimizer's output, with the default setting achieving better results. Top instructions with high accuracies are found in prompt optimization for various tasks. The design choices and
The study examines various aspects of leveraging large language models (LLMs) for optimization. It explores the effect of presenting accuracy scores and exemplars in the meta-prompt, the number of generated instructions per step, the starting point for prompt optimization, the
The optimizer LLM in this work generates new instructions at each optimization step, without imitating past instructions. The optimization process incorporates past generated instructions with their scores, allowing the optimizer LLM to discover common patterns of high-quality instructions. Previous work has shown
This text excerpt includes a list of research papers and articles related to the optimization of large language models. The papers cover various topics such as code-level neural architecture search, code generation with natural language feedback, chain-of-thought prompting for chat models, instruction
This document is a compilation of references to various papers related to leveraging large language models for optimization. The referenced papers cover topics such as the ability of language models to solve computer tasks, optimization methods like Adam, the use of large language models as zero-shot
This summary provides a list of references to research papers and articles related to optimization and language models. The references include topics such as reinforcement learning for vehicle routing problems, self-repair for code generation, optimization algorithms, instruction search for language models, prompt optimization
Large Language Models (LLMs) have shown success in optimizing basic math problems and prompts. However, there are limitations that hinder their ability to solve more challenging problems. These limitations include LLMs hallucinating values in math calculations and generating solutions that have
The optimizer model can get stuck when it only proposes to increase or decrease certain variables, and doing so would not improve the objective value. To mitigate this issue, the model samples multiple new solutions at each step for more exploration. However, navigating a complex
The document discusses prompt optimization using large language models. Different optimizer models work best with different styles of meta-prompts. Examples of meta-prompts for various models are shown. The goal is to generate concise and effective instructions for solving problems. Prompt optimization is
The summary of the text is as follows: The text consists of a series of numbers followed by a table of instructions for various tasks. The tasks include boolean expressions, causal judgement, date understanding, disambiguation-qa, dyck languages,
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The study found that the instructions generated by large language models outperformed the baseline and starting point instructions on various tasks. Accuracies for different tasks were measured using the PaLM 2-L scorer and the gpt-3.5-tur