Evolutionary Large Language Model for Automated Feature Transformation

• URL: http://arxiv.org/abs/2405.16203v1
• Date: Sat, 25 May 2024 12:27:21 GMT
• Title: Evolutionary Large Language Model for Automated Feature Transformation
• Authors: Nanxu Gong, Chandan K. Reddy, Wangyang Ying, Yanjie Fu,
• Abstract summary: We propose an evolutionary Large Language Model (LLM) framework for automated feature transformation. This framework consists of two parts: 1) constructing a multi-population database through an RL data collector, and 2) utilizing the ability of Large Language Model (LLM) in sequence understanding. We empirically demonstrate the effectiveness and generality of our proposed method.
• Score: 25.956740176321897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feature transformation aims to reconstruct the feature space of raw features to enhance the performance of downstream models. However, the exponential growth in the combinations of features and operations poses a challenge, making it difficult for existing methods to efficiently explore a wide space. Additionally, their optimization is solely driven by the accuracy of downstream models in specific domains, neglecting the acquisition of general feature knowledge. To fill this research gap, we propose an evolutionary LLM framework for automated feature transformation. This framework consists of two parts: 1) constructing a multi-population database through an RL data collector while utilizing evolutionary algorithm strategies for database maintenance, and 2) utilizing the ability of Large Language Model (LLM) in sequence understanding, we employ few-shot prompts to guide LLM in generating superior samples based on feature transformation sequence distinction. Leveraging the multi-population database initially provides a wide search scope to discover excellent populations. Through culling and evolution, the high-quality populations are afforded greater opportunities, thereby furthering the pursuit of optimal individuals. Through the integration of LLMs with evolutionary algorithms, we achieve efficient exploration within a vast space, while harnessing feature knowledge to propel optimization, thus realizing a more adaptable search paradigm. Finally, we empirically demonstrate the effectiveness and generality of our proposed method.

Related papers

• Heuristically Adaptive Diffusion-Model Evolutionary Strategy [1.8299322342860518]
  Diffusion Models represent a significant advancement in generative modeling. Our research reveals a fundamental connection between diffusion models and evolutionary algorithms. Our framework marks a major algorithmic transition, offering increased flexibility, precision, and control in evolutionary optimization processes.
  arXiv  Detail & Related papers  (2024-11-20T16:06:28Z)
• Deep Insights into Automated Optimization with Large Language Models and Evolutionary Algorithms [3.833708891059351]
  Large Language Models (LLMs) and Evolutionary Algorithms (EAs) offer promising new approach to overcome limitations and make optimization more automated. LLMs act as dynamic agents that can generate, refine, and interpret optimization strategies. EAs efficiently explore complex solution spaces through evolutionary operators.
  arXiv  Detail & Related papers  (2024-10-28T09:04:49Z)
• Large Language Models as Surrogate Models in Evolutionary Algorithms: A Preliminary Study [5.6787965501364335]
  Surrogate-assisted selection is a core step in evolutionary algorithms to solve expensive optimization problems. Traditionally, this has relied on conventional machine learning methods, leveraging historical evaluated evaluations to predict the performance of new solutions. In this work, we propose a novel surrogate model based purely on LLM inference capabilities, eliminating the need for training.
  arXiv  Detail & Related papers  (2024-06-15T15:54:00Z)
• Discovering Preference Optimization Algorithms with and for Large Language Models [50.843710797024805]
  offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. We perform objective discovery to automatically discover new state-of-the-art preference optimization algorithms without (expert) human intervention. Experiments demonstrate the state-of-the-art performance of DiscoPOP, a novel algorithm that adaptively blends logistic and exponential losses.
  arXiv  Detail & Related papers  (2024-06-12T16:58:41Z)
• Self-Exploring Language Models: Active Preference Elicitation for Online Alignment [88.56809269990625]
  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 fine-tuned on Zephyr-7B-SFT and Llama-3-8B-Instruct models, Self-Exploring Language Models (SELM) significantly boosts the performance on instruction-following benchmarks.
  arXiv  Detail & Related papers  (2024-05-29T17:59:07Z)
• Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization [15.476478159958416]
  We employ a large language model (LLM) to enhance evolutionary search for solving constrained multi-objective optimization problems. Our aim is to speed up the convergence of the evolutionary population.
  arXiv  Detail & Related papers  (2024-05-09T13:44:04Z)
• 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)
• How Can LLM Guide RL? A Value-Based Approach [68.55316627400683]
  Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. Recent developments in large language models (LLMs) have showcased impressive capabilities in language understanding and generation, yet they fall short in exploration and self-improvement capabilities. We develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning.
  arXiv  Detail & Related papers  (2024-02-25T20:07:13Z)
• 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)
• 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)
• Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers [70.18534453485849]
  EvoPrompt is a framework for discrete prompt optimization. It borrows the idea of evolutionary algorithms (EAs) as they exhibit good performance and fast convergence. It significantly outperforms human-engineered prompts and existing methods for automatic prompt generation.
  arXiv  Detail & Related papers  (2023-09-15T16:50:09Z)

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.