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LLMs fine-tuned to predict neural network performance from code

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a method to fine-tune Large Language Models (LLMs) for predicting neural network performance on image classification tasks. By analyzing neural network architecture code, an LLM can determine which of two datasets a network will perform better on. This approach, integrated into the NNGPT framework and tested on the LEMUR dataset, showed that LLMs can extract more predictive signal from code than from dataset metadata alone. AI

IMPACT Demonstrates LLMs' capability to reason about neural network code, potentially improving AutoML efficiency.

RANK_REASON Academic paper detailing a new method for fine-tuning LLMs for a specific classification task.

Read on arXiv cs.LG →

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

LLMs fine-tuned to predict neural network performance from code

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Academic paper detailing a new method for fine-tuning LLMs for a specific classification task.
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paper, model release
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189 days old
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COVERAGE [2]

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

    From Code to Prediction: Fine-Tuning LLMs for Neural Network Performance Classification in NNGPT

    Automated Machine Learning (AutoML) frameworks increasingly leverage Large Language Models (LLMs) for tasks such as hyperparameter optimization and neural architecture code generation. However, current LLM-based approaches focus on generative outputs and evaluate them by training…

  2. arXiv cs.CV TIER_1 English(EN) · Mahmoud Hanouneh, Radu Timofte, Dmitry Ignatov ·

    From Code to Prediction: Fine-Tuning LLMs for Neural Network Performance Classification in NNGPT

    arXiv:2605.03686v1 Announce Type: cross Abstract: Automated Machine Learning (AutoML) frameworks increasingly leverage Large Language Models (LLMs) for tasks such as hyperparameter optimization and neural architecture code generation. However, current LLM-based approaches focus o…

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