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LLMs fine-tuned to predict neural network performance from code

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 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

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

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 →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · 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 · 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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