Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models

• URL: http://arxiv.org/abs/2404.06290v2
• Date: Sat, 6 Jul 2024 08:28:42 GMT
• Title: Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models
• Authors: Beichen Huang, Xingyu Wu, Yu Zhou, Jibin Wu, Liang Feng, Ran Cheng, Kay Chen Tan,
• Abstract summary: Large language models (LLMs) have demonstrated exceptional performance in natural language processing tasks. This paper endeavors to offer deep insights into the potential of LLMs in optimization. Our findings reveal both the limitations and advantages of LLMs in optimization.
• Score: 32.859634302766146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated exceptional performance not only in natural language processing tasks but also in a great variety of non-linguistic domains. In diverse optimization scenarios, there is also a rising trend of applying LLMs. However, whether the application of LLMs in the black-box optimization problems is genuinely beneficial remains unexplored. This paper endeavors to offer deep insights into the potential of LLMs in optimization through a comprehensive investigation, which covers both discrete and continuous optimization problems to assess the efficacy and distinctive characteristics that LLMs bring to this field. Our findings reveal both the limitations and advantages of LLMs in optimization. Specifically, on the one hand, despite the significant power consumed for running the models, LLMs exhibit subpar performance in pure numerical tasks, primarily due to a mismatch between the problem domain and their processing capabilities; on the other hand, although LLMs may not be ideal for traditional numerical optimization, their potential in broader optimization contexts remains promising, where LLMs exhibit the ability to solve problems in non-numerical domains and can leverage heuristics from the prompt to enhance their performance. To the best of our knowledge, this work presents the first systematic evaluation of LLMs for numerical optimization. Our findings pave the way for a deeper understanding of LLMs' role in optimization and guide future application of LLMs in a wide range of scenarios.

Related papers

• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
  Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving. Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods. We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
  arXiv  Detail & Related papers  (2024-10-10T17:00:06Z)
• EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization [73.7779735046424]
  We show that different prompts should be adapted to different Large Language Models (LLM) to enhance their capabilities across various downstream tasks in NLP. We then propose a model-adaptive prompt (MAPO) method that optimize the original prompts for each specific LLM in downstream tasks.
  arXiv  Detail & Related papers  (2024-07-04T18:39:59Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• Revisiting OPRO: The Limitations of Small-Scale LLMs as Optimizers [15.809293135844756]
  We revisit OPRO for automated prompting with relatively small-scale Language Models (LLMs) OPRO shows limited effectiveness in small-scale LLMs, with limited inference capabilities constraining optimization ability. We suggest future automatic prompting engineering to consider both model capabilities and computational costs.
  arXiv  Detail & Related papers  (2024-05-16T17:33:50Z)
• Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism [1.1463861912335864]
  Large Language Models (LLMs) have demonstrated remarkable reasoning abilities. This paper asserts that LLMs possess the capability for zero-shot optimization across diverse scenarios. We introduce a novel population-based method for numerical optimization using LLMs.
  arXiv  Detail & Related papers  (2024-03-04T13:57:37Z)
• How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems [33.33996058215666]
  Large language models (LLMs) have positioned themselves as capable tools for addressing complex optimization challenges. We propose to enhance the optimization performance using multimodal LLM capable of processing both textual and visual prompts.
  arXiv  Detail & Related papers  (2024-03-04T06:24:21Z)
• Are Large Language Models Good Prompt Optimizers? [65.48910201816223]
  We conduct a study to uncover the actual mechanism of LLM-based Prompt Optimization. Our findings reveal that the LLMs struggle to identify the true causes of errors during reflection, tending to be biased by their own prior knowledge. We introduce a new "Automatic Behavior Optimization" paradigm, which directly optimize the target model's behavior in a more controllable manner.
  arXiv  Detail & Related papers  (2024-02-03T09:48:54Z)
• Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity [61.54815512469125]
  This survey addresses the crucial issue of factuality in Large Language Models (LLMs) As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital.
  arXiv  Detail & Related papers  (2023-10-11T14:18:03Z)
• Towards Optimizing with Large Language Models [3.80039497875781]
  We conduct an assessment of the optimization capabilities of LLMs across various tasks and data sizes. We introduce three distinct metrics for a comprehensive assessment of task performance from various perspectives.
  arXiv  Detail & Related papers  (2023-10-08T15:35:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.