[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119633-en":3,"doc-seo-119633-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119633,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Towards Automated Machine Learning Research - A Top-Down Framework Using Large Language Models","A paper proposes a top-down approach to accelerating incremental machine learning research through component-level innovation driven by Large Language Models (LLMs). The framework generates candidate novel components, validates feasibility, and evaluates performance against established baselines. A key difference from AutoML and NAS is using cross-domain knowledge from LLMs to propose components beyond hardcoded sets. A reward model prioritizes promising hypotheses to improve hypothesis generation and evaluation efficiency.","Towards Automated Machine Learning Research  \nShervin Ardeshir  \narXiv :2409 .05258v 1 [ cs .LG] 9 Sep 2024  \nAbstract  \nThis paper explores a top-down approach to automating incremental advances in machine learning research through component-level innovation, facilitated by Large Language Models (LLMs) . Our framework systematically generates novel components, validates their feasibility, and evaluates their performance against existing baselines. A key distinction of this approach lies in how these novel components are generated. Unlike traditional AutoML and NAS methods, which often rely on a bottom-up combinatorial search over predefined, hardcoded base components, our method leverages the cross-domain knowledge embedded in LLMs to propose new components that may not be confined to any hardcoded predefined set. By incorporating a reward model to prioritize promising hypotheses, we aim to improve the efficiency of the hypothesis generation and evaluation process. We hope this approach offers a new avenue for exploration and contributes to the ongoing dialogue in the field.  \nIntroduction  \nEfficient hypothesis generation, validation, and evaluation are critical, yet resource-intensive, components of scientific discovery. In many scientific fields, these processes require substantial manual effort, as they often involve intricate experiments and extensive data collection. The ability to streamline these tasks could significantly accelerate the pace of innovation.  \nMachine learning offers a unique opportunity in this regard. Unlike other scientific domains, hypothesis validation in machine learning can be automated through code, with effectiveness measured numerically using objective criteria such as loss or accuracy. This capability makes machine learning an ideal field for exploring automation in research. Building on this potential, we propose a framework that leverages a top-down methodology using Large Language Models (LLMs) to generate high-level hypotheses. Although our approach is not intended to replace bottomup methods such as AutoML-Zero(Real et al. 2020) or MetaQNN(Santoro et al. 2016), it offers a complementary path by introducing cross-domain innovation and a broader exploration of potential solutions. By formulating and testing hypotheses in natural language, our method lowers the  \nCopyright © 2025, Association for the Advancement of Artificial Intelligence ([www.aaai.org](www.aaai.org)). All rights reserved.  \nbarrier to entry for a wider range of researchers, fostering interdisciplinary collaboration and the integration of diverse knowledge from various fields. This combination of top-down and bottom-up strategies improves the research pipeline, providing a more comprehensive and innovative approach to automated machine learning research.  \nIn this paper, we contribute by:  \n• Proposing and evaluating viable components: Generating viable hypotheses to replace neural network components and achieve competitive performance with known alternatives.  \n• Training a reward model: Learning patterns between the content of a hypothesis and its downstream performance.  \n• Efficient Hypothesis Generation: Using the reward model to prune and prioritize hypotheses, improving the efficiency of generation, validation, and evaluation.  \nCaveats  \n1. This work does not make any assumptions about the inherent capabilities of LLMs to reason or have a deep understanding of ML topics. Even a random string generator can yield a meaningful hypothesis given unlimited attempts, akin to the infinite monkey theorem, which suggests that a monkey hitting keys at random on a typewriter for an infinite amount of time will almost surely type a given text, such as the complete works of Shakespeare. Our assumptions on the state of LLMs and ML are as follows.  \n(a) LLMs are good enough at generating feasible outputs, thus narrowing down our search space meaningfully from a set of random outputs.  \n(b) LLMs (and ML models in general) are good ","cbCaie0zpXqXnRaZ","https://ap.wps.com/l/cbCaie0zpXqXnRaZ","pdf",1331126,1,16,"English","en",105,"# Introduction\n## Hypothesis generation and validation as automation targets\n## Complementing AutoML and NAS with top-down exploration\n# Contributions\n## Viable component generation\n## Training and using a reward model\n## Efficient hypothesis generation via pruning\n# Caveats\n## Assumptions about LLM capability\n## Scope and claims limitations\n## Resource and dataset constraints","[{\"question\":\"What is the paper’s main idea for automated machine learning research?\",\"answer\":\"It introduces a top-down framework where LLMs generate high-level hypotheses for incremental component innovations, followed by validation and performance evaluation against baselines.\"},{\"question\":\"How does the method differ from traditional AutoML and NAS approaches?\",\"answer\":\"Instead of bottom-up combinatorial search over predefined components, it leverages cross-domain knowledge in LLMs to propose novel components that may lie outside hardcoded sets.\"},{\"question\":\"Why is a reward model used in this framework?\",\"answer\":\"A reward model learns patterns relating hypothesis content to downstream performance, allowing it to rank and prune hypotheses to improve the efficiency of generation, validation, and evaluation.\"}]","Towards Automated Machine Learning Research - 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