Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Rephrase and Contrast: Fine-Tuning Language Models for Enhanced Understanding of Communication and Computer Networks

Liujianfu Wang, Yuyang Du, Jingqi Lin, Kexin Chen, Soung Chang Liew

TL;DR

Rephrase and Contrast (RaC) framework is introduced, an efficient fine-tuning framework that enhances LLMs’ comprehension and critical thinking abilities by incorporating question reformulation and contrastive analysis of correct and incorrect answers during the fine-tuning process.

Abstract

Large language models (LLMs) are being widely researched across various disciplines, with significant recent efforts focusing on adapting LLMs for understanding of how communication networks operate. However, over-reliance on prompting techniques hinders the full exploitation of the generalization ability of these models, and the lack of efficient fine-tuning methods prevents the full realization of lightweight LLMs' potential. This paper addresses these challenges by introducing our Rephrase and Contrast (RaC) framework, an efficient fine-tuning framework. RaC enhances LLMs' comprehension and critical thinking abilities by incorporating question reformulation and contrastive analysis of correct and incorrect answers during the fine-tuning process. Experimental results demonstrate a 63.73% accuracy improvement over the foundational model when tested on a comprehensive networking problem set. Moreover, to efficiently construct the dataset for RaC fine-tuning, we develop a GPT-assisted data mining method for generating high-quality question-answer (QA) pairs; furthermore, we introduce ChoiceBoost, a data augmentation technique that expands dataset size while reducing answer-order bias. Apart from these technical innovations, we contribute to the networking community by open-sourcing valuable research resources, including: 1) the fine-tuned networking model referred to as RaC-Net, 2) the training dataset used for fine-tuning the model, 3) three testing problem sets of different difficulties to serve as benchmarks for future research, and 4) code associated with the above resources.

Rephrase and Contrast: Fine-Tuning Language Models for Enhanced Understanding of Communication and Computer Networks

TL;DR

Rephrase and Contrast (RaC) framework is introduced, an efficient fine-tuning framework that enhances LLMs’ comprehension and critical thinking abilities by incorporating question reformulation and contrastive analysis of correct and incorrect answers during the fine-tuning process.

Abstract

Large language models (LLMs) are being widely researched across various disciplines, with significant recent efforts focusing on adapting LLMs for understanding of how communication networks operate. However, over-reliance on prompting techniques hinders the full exploitation of the generalization ability of these models, and the lack of efficient fine-tuning methods prevents the full realization of lightweight LLMs' potential. This paper addresses these challenges by introducing our Rephrase and Contrast (RaC) framework, an efficient fine-tuning framework. RaC enhances LLMs' comprehension and critical thinking abilities by incorporating question reformulation and contrastive analysis of correct and incorrect answers during the fine-tuning process. Experimental results demonstrate a 63.73% accuracy improvement over the foundational model when tested on a comprehensive networking problem set. Moreover, to efficiently construct the dataset for RaC fine-tuning, we develop a GPT-assisted data mining method for generating high-quality question-answer (QA) pairs; furthermore, we introduce ChoiceBoost, a data augmentation technique that expands dataset size while reducing answer-order bias. Apart from these technical innovations, we contribute to the networking community by open-sourcing valuable research resources, including: 1) the fine-tuned networking model referred to as RaC-Net, 2) the training dataset used for fine-tuning the model, 3) three testing problem sets of different difficulties to serve as benchmarks for future research, and 4) code associated with the above resources.
Paper Structure (9 sections, 6 figures)

This paper contains 9 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. RaC: An Efficient Fine-tuning Framework
  4. Training and Testing Datasets
  5. Dataset Construction
  6. Data Resources Released
  7. Experiments
  8. Conclusion
  9. Acknowledgment

Figures (6)

  • Figure 1: An example of training data that meets the data structure requirements of our RaC framework. For easier understanding and better assessment of data quality, comments have been added in the grey box. These comments are not part of the training dataset; they are included for illustrative purposes only.
  • Figure 2: LLM prompt template used by RaC-Net for creating QA pairs.
  • Figure 3: An overview of the released data resource. The three pie charts share the same components listed in the left legend. The legend uses a grayscale graph to indicate each component, with the darker shade corresponding to the deeper-color segments in pie charts (e.g., the darkest blue, orange, and green sections match the darkest gray category, which represents the Network layer and Routing).
  • Figure 4: Accuracies of models of fold index 1 to 10 tested on the easy problem set. Due to the limitation in our computational resources, we randomly selected 10 folds among the 20 candidate folds for concept proofing.
  • Figure 5: Accuracies of baseline models tested on easy, hard, and comprehensive testing problem sets.
  • ...and 1 more figures