LLM-Guided Evolution: An Autonomous Model Optimization for Object Detection

• URL: http://arxiv.org/abs/2504.02280v1
• Date: Thu, 03 Apr 2025 05:06:06 GMT
• Title: LLM-Guided Evolution: An Autonomous Model Optimization for Object Detection
• Authors: YiMing Yu, Jason Zutty,
• Abstract summary: In machine learning, Neural Architecture Search (NAS) requires domain knowledge of model design and a large amount of trial-and-error to achieve promising performance.<n>The Large Language Model (LLM)-Guided Evolution (GE) framework transformed this approach by incorporating LLMs to directly modify model source code for image classification algorithms on CIFAR data.<n>We show that LLM-GE produced variants with significant performance improvements, such as an increase in Mean Average Precision from 92.5% to 94.5%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In machine learning, Neural Architecture Search (NAS) requires domain knowledge of model design and a large amount of trial-and-error to achieve promising performance. Meanwhile, evolutionary algorithms have traditionally relied on fixed rules and pre-defined building blocks. The Large Language Model (LLM)-Guided Evolution (GE) framework transformed this approach by incorporating LLMs to directly modify model source code for image classification algorithms on CIFAR data and intelligently guide mutations and crossovers. A key element of LLM-GE is the "Evolution of Thought" (EoT) technique, which establishes feedback loops, allowing LLMs to refine their decisions iteratively based on how previous operations performed. In this study, we perform NAS for object detection by improving LLM-GE to modify the architecture of You Only Look Once (YOLO) models to enhance performance on the KITTI dataset. Our approach intelligently adjusts the design and settings of YOLO to find the optimal algorithms against objective such as detection accuracy and speed. We show that LLM-GE produced variants with significant performance improvements, such as an increase in Mean Average Precision from 92.5% to 94.5%. This result highlights the flexibility and effectiveness of LLM-GE on real-world challenges, offering a novel paradigm for automated machine learning that combines LLM-driven reasoning with evolutionary strategies.

Related papers

• Efficient Model Selection for Time Series Forecasting via LLMs [52.31535714387368]
  We propose to leverage Large Language Models (LLMs) as a lightweight alternative for model selection.<n>Our method eliminates the need for explicit performance matrices by utilizing the inherent knowledge and reasoning capabilities of LLMs.
  arXiv  Detail & Related papers  (2025-04-02T20:33:27Z)
• Exploring Model Editing for LLM-based Aspect-Based Sentiment Classification [17.512415475301395]
  We investigate model editing to serve an efficient method for adapting large language models (LLMs) to solve aspect-based sentiment classification.<n>Our findings reveal that a distinct set of mid-layer representations is essential for detecting the sentiment polarity of given aspect words.<n>We develop a model editing approach that focuses exclusively on these critical parts of the LLM, leading to a more efficient method for adapting LLMs.
  arXiv  Detail & Related papers  (2025-03-19T11:21:37Z)
• LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers [10.282327560070202]
  Large Language Models (LLMs) have enabled the integration of domain knowledge into the feature engineering process. We propose LLM-FE, a novel framework that combines evolutionary search with the domain knowledge and reasoning capabilities of LLMs. Our results demonstrate that LLM-FE consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-03-18T17:11:24Z)
• IMPROVE: Iterative Model Pipeline Refinement and Optimization Leveraging LLM Agents [17.301758094000125]
  Large language model (LLM) agents have emerged as a promising solution to automate the development of computer vision models.<n>We introduce Iterative Refinement, a novel strategy for LLM-driven ML pipeline design.<n>Iterative Refinement improves stability, interpretability, and overall model performance.
  arXiv  Detail & Related papers  (2025-02-25T01:52:37Z)
• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression. LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model. Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• Improving Autoregressive Visual Generation with Cluster-Oriented Token Prediction [52.09472099976885]
  IAR is an Improved AutoRegressive Visual Generation Method that enhances the training efficiency and generation quality of LLM-based visual generation models.<n>Our method consistently enhances the model training efficiency and performance from 100M to 1.4B, reducing the training time by half while achieving the same FID.
  arXiv  Detail & Related papers  (2025-01-01T15:58:51Z)
• Multi-Objective Large Language Model Unlearning [3.372396620898397]
  Gradient Ascent (GA) is a proactive way to decrease the prediction probability of the model on the target data.<n>We propose Multi-Objective Large Language Model Unlearning (MOLLM) algorithm to overcome gradient explosion and catastrophic forgetting.<n>Our empirical results verify that MoLLM outperforms the SOTA GA-based LLM unlearning methods in terms of unlearning effect and model utility preservation.
  arXiv  Detail & Related papers  (2024-12-29T09:35:56Z)
• Search for Efficient Large Language Models [52.98684997131108]
  Large Language Models (LLMs) have long held sway in the realms of artificial intelligence research. Weight pruning, quantization, and distillation have been embraced to compress LLMs, targeting memory reduction and inference acceleration. Most model compression techniques concentrate on weight optimization, overlooking the exploration of optimal architectures.
  arXiv  Detail & Related papers  (2024-09-25T21:32:12Z)
• CoMMIT: Coordinated Instruction Tuning for Multimodal Large Language Models [68.64605538559312]
  In this paper, we analyze the MLLM instruction tuning from both theoretical and empirical perspectives. Inspired by our findings, we propose a measurement to quantitatively evaluate the learning balance. In addition, we introduce an auxiliary loss regularization method to promote updating of the generation distribution of MLLMs.
  arXiv  Detail & Related papers  (2024-07-29T23:18:55Z)
• One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
  Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs)<n>We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
  arXiv  Detail & Related papers  (2024-05-30T03:44:54Z)
• LLM Guided Evolution - The Automation of Models Advancing Models [0.0]
  "Guided Evolution" (GE) is a novel framework that diverges from traditional machine learning approaches. "Evolution of Thought" (EoT) enhances GE by enabling LLMs to reflect on and learn from the outcomes of previous mutations. Our application of GE in evolving the ExquisiteNetV2 model demonstrates its efficacy.
  arXiv  Detail & Related papers  (2024-03-18T03:44:55Z)
• 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)
• Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark [166.40879020706151]
  This paper proposes a shift towards BP-free, zeroth-order (ZO) optimization as a solution for reducing memory costs during fine-tuning. Unlike traditional ZO-SGD methods, our work expands the exploration to a wider array of ZO optimization techniques. Our study unveils previously overlooked optimization principles, highlighting the importance of task alignment, the role of the forward gradient method, and the balance between algorithm complexity and fine-tuning performance.
  arXiv  Detail & Related papers  (2024-02-18T14:08:48Z)

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.