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LLMs guide neural network generation, improving accuracy via source-model guidance · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel protocol for using large language models (LLMs) to improve existing neural networks by guiding the generation process with a stronger, same-family source model. This method aims to disentangle transfer learning from adaptation, ensuring that generated modifications are both valid and accurate. The protocol demonstrated significant accuracy gains on datasets like CIFAR-10 and SVHN, outperforming non-source guided candidates and showing that the LLM adapts rather than simply copies the source model's architecture. AI

IMPACT This research could lead to more efficient and effective methods for adapting and improving existing neural network models using LLMs.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new method for LLM-driven neural network generation.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs guide neural network generation, improving accuracy via source-model guidance · 2 sources tracked

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Kabir Dev Paul Baghel, Radu Timofte, Dmitry Ignatov ·

    LLM-Driven Neural Network Generation with Same-Family Architecture Guidance: Disentangling Transfer and Adaptation

    arXiv:2607.05704v1 Announce Type: new Abstract: Large language models (LLMs) can generate neural-network modifications, but unrestricted generation is often invalid or harmful. This paper studies a narrower setting: improving a weak target model using a stronger same-family sourc…

  2. arXiv cs.LG TIER_1 English(EN) · Dmitry Ignatov ·

    LLM-Driven Neural Network Generation with Same-Family Architecture Guidance: Disentangling Transfer and Adaptation

    Large language models (LLMs) can generate neural-network modifications, but unrestricted generation is often invalid or harmful. This paper studies a narrower setting: improving a weak target model using a stronger same-family source model from a neural-network database. We propo…

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