Large Language Model Assisted Adversarial Robustness Neural Architecture Search
- URL: http://arxiv.org/abs/2406.05433v1
- Date: Sat, 8 Jun 2024 10:45:07 GMT
- Title: Large Language Model Assisted Adversarial Robustness Neural Architecture Search
- Authors: Rui Zhong, Yang Cao, Jun Yu, Masaharu Munetomo,
- Abstract summary: This paper proposes a novel LLM-assisted (LLMO) to address adversarial neural architecture search (ARNAS)
We design prompt using the standard CRISPE framework (i.e., Capacity and Role, Insight, Statement, Personality, and Experiment)
We iteratively refine the prompt, and the responses from Gemini are adapted as solutions to ARNAS instances.
- Score: 14.122460940115069
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Motivated by the potential of large language models (LLMs) as optimizers for solving combinatorial optimization problems, this paper proposes a novel LLM-assisted optimizer (LLMO) to address adversarial robustness neural architecture search (ARNAS), a specific application of combinatorial optimization. We design the prompt using the standard CRISPE framework (i.e., Capacity and Role, Insight, Statement, Personality, and Experiment). In this study, we employ Gemini, a powerful LLM developed by Google. We iteratively refine the prompt, and the responses from Gemini are adapted as solutions to ARNAS instances. Numerical experiments are conducted on NAS-Bench-201-based ARNAS tasks with CIFAR-10 and CIFAR-100 datasets. Six well-known meta-heuristic algorithms (MHAs) including genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), and its variants serve as baselines. The experimental results confirm the competitiveness of the proposed LLMO and highlight the potential of LLMs as effective combinatorial optimizers. The source code of this research can be downloaded from \url{https://github.com/RuiZhong961230/LLMO}.
Related papers
- Solving General Natural-Language-Description Optimization Problems with Large Language Models [34.50671063271608]
We propose a novel framework called OptLLM that augments LLMs with external solvers.
OptLLM accepts user queries in natural language, convert them into mathematical formulations and programming codes, and calls the solvers to calculate the results.
Some features of OptLLM framework have been available for trial since June 2023.
arXiv Detail & Related papers (2024-07-09T07:11:10Z) - Two Optimizers Are Better Than One: LLM Catalyst Empowers Gradient-Based Optimization for Prompt Tuning [69.95292905263393]
We show that gradient-based optimization and large language models (MsLL) are complementary to each other, suggesting a collaborative optimization approach.
Our code is released at https://www.guozix.com/guozix/LLM-catalyst.
arXiv Detail & Related papers (2024-05-30T06:24:14Z) - Self-Exploring Language Models: Active Preference Elicitation for Online Alignment [90.4820014819937]
We propose a bilevel objective optimistically biased towards potentially high-reward responses to actively explore out-of-distribution regions.
Our experimental results demonstrate that when finetuned on Zephyr-7B-SFT and Llama-3-8B-Instruct models, SELM significantly boosts the performance on instruction-following benchmarks.
arXiv Detail & Related papers (2024-05-29T17:59:07Z) - 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) - Large Language Models As Evolution Strategies [6.873777465945062]
In this work, we investigate whether large language models (LLMs) are in principle capable of implementing evolutionary optimization algorithms.
We introduce a novel prompting strategy, consisting of least-to-most sorting of discretized population members.
We find that our setup allows the user to obtain an LLM-based evolution strategy, which we call EvoLLM', that robustly outperforms baseline algorithms.
arXiv Detail & Related papers (2024-02-28T15:02:17Z) - Unleashing the Potential of Large Language Models as Prompt Optimizers: An Analogical Analysis with Gradient-based Model Optimizers [108.72225067368592]
We propose a novel perspective to investigate the design of large language models (LLMs)-based prompts.
We identify two pivotal factors in model parameter learning: update direction and update method.
In particular, we borrow the theoretical framework and learning methods from gradient-based optimization to design improved strategies.
arXiv Detail & Related papers (2024-02-27T15:05:32Z) - Can LLMs Configure Software Tools [0.76146285961466]
In software engineering, the meticulous configuration of software tools is crucial in ensuring optimal performance within intricate systems.
In this study, we embark on an exploration of leveraging Large-Language Models (LLMs) to streamline the software configuration process.
Our work presents a novel approach that employs LLMs, such as Chat-GPT, to identify starting conditions and narrow down the search space, improving configuration efficiency.
arXiv Detail & Related papers (2023-12-11T05:03:02Z) - Large Language Models as Evolutionary Optimizers [37.92671242584431]
We present the first study on large language models (LLMs) as evolutionarys.
The main advantage is that it requires minimal domain knowledge and human efforts, as well as no additional training of the model.
We also study the effectiveness of the self-adaptation mechanism in evolutionary search.
arXiv Detail & Related papers (2023-10-29T15:44:52Z) - Query-Dependent Prompt Evaluation and Optimization with Offline Inverse
RL [62.824464372594576]
We aim to enhance arithmetic reasoning ability of Large Language Models (LLMs) through zero-shot prompt optimization.
We identify a previously overlooked objective of query dependency in such optimization.
We introduce Prompt-OIRL, which harnesses offline inverse reinforcement learning to draw insights from offline prompting demonstration data.
arXiv Detail & Related papers (2023-09-13T01:12:52Z) - ZARTS: On Zero-order Optimization for Neural Architecture Search [94.41017048659664]
Differentiable architecture search (DARTS) has been a popular one-shot paradigm for NAS due to its high efficiency.
This work turns to zero-order optimization and proposes a novel NAS scheme, called ZARTS, to search without enforcing the above approximation.
In particular, results on 12 benchmarks verify the outstanding robustness of ZARTS, where the performance of DARTS collapses due to its known instability issue.
arXiv Detail & Related papers (2021-10-10T09:35:15Z) - Off-Policy Reinforcement Learning for Efficient and Effective GAN
Architecture Search [50.40004966087121]
We introduce a new reinforcement learning based neural architecture search (NAS) methodology for generative adversarial network (GAN) architecture search.
The key idea is to formulate the GAN architecture search problem as a Markov decision process (MDP) for smoother architecture sampling.
We exploit an off-policy GAN architecture search algorithm that makes efficient use of the samples generated by previous policies.
arXiv Detail & Related papers (2020-07-17T18:29:17Z)
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.