Fine-Tuning Pre-Trained Large Language Models for Price Prediction on Network Freight Platforms

Pengfei Lu, Ping Zhang (), Jun Wu, Xia Wu, Yunsheng Mao and Tao Liu
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Pengfei Lu: School of Computer and Information, Anhui Polytechnic University, Wuhu 241000, China
Ping Zhang: School of Computer and Information, Anhui Polytechnic University, Wuhu 241000, China
Jun Wu: School of Computer and Information, Anhui Polytechnic University, Wuhu 241000, China
Xia Wu: School of Computer and Information, Anhui Polytechnic University, Wuhu 241000, China
Yunsheng Mao: School of Computer and Information, Anhui Polytechnic University, Wuhu 241000, China
Tao Liu: School of Computer and Information, Anhui Polytechnic University, Wuhu 241000, China

Mathematics, 2025, vol. 13, issue 15, 1-28

Abstract: Various factors influence the formation and adjustment of network freight prices, including transportation costs, cargo characteristics, and policies and regulations. The interaction of these factors increases the difficulty of accurately predicting network freight prices through regressions or other machine learning models, especially when the amount and quality of training data are limited. This paper introduces large language models (LLMs) to predict network freight prices using their inherent prior knowledge. Different data sorting methods and serialization strategies are employed to construct the corpora of LLMs, which are then tested on multiple base models. A few-shot sample dataset is constructed to test the performance of models under insufficient information. The Chain of Thought (CoT) is employed to construct a corpus that demonstrates the reasoning process in freight price prediction. Cross entropy loss with LoRA fine-tuning and cosine annealing learning rate adjustment, and Mean Absolute Error (MAE) loss with full fine-tuning and OneCycle learning rate adjustment to train the models, respectively, are used. The experimental results demonstrate that LLMs are better than or competitive with the best comparison model. Tests on a few-shot dataset demonstrate that LLMs outperform most comparison models in performance. This method provides a new reference for predicting network freight prices.

Keywords: network freight; price prediction; LLMs; few-shot learning; transfer learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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