LLMs fine-tuned to predict neural network performance from code

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-05 12:30

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

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

排序理由 Academic paper detailing a new method for fine-tuning LLMs for a specific classification task.

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报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Dmitry Ignatov · 2026-05-05 12:30

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…
arXiv cs.CV TIER_1 English(EN) · Mahmoud Hanouneh, Radu Timofte, Dmitry Ignatov · 2026-05-06 04:00

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…

报道来源 [2]

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

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

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