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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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A criteria-based classification model using augmentation and contrastive learning for analyzing imbalanced statement

Junho Shin1, Jinhee Kwak1, Jaehee Jung1

  • 1Department of Information and Communication Engineering, University of Myongji, Yongin, Gyeonggi-do, South Korea.

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|July 18, 2024
PubMed
Summary
This summary is machine-generated.

This study developed an objective model for Criteria Based Content Analysis (CBCA) using NLP, significantly reducing human subjectivity in victim statement analysis for improved legal accuracy.

Keywords:
Criteria-based content analysisData augmentationDual contrastive learningSMBO optimizer

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Area of Science:

  • Forensic Psychology
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Criteria Based Content Analysis (CBCA) is vital for evaluating victim statement authenticity.
  • Subjectivity in human analysis can impact the reliability of testimony evaluation.
  • Existing CBCA methods face challenges with imbalanced data distribution.

Purpose of the Study:

  • To develop an objective, natural language processing (NLP)-based classification model for CBCA statement analysis.
  • To enhance the accuracy and reliability of CBCA by minimizing human subjectivity.
  • To address data imbalance issues in criterion classifications.

Main Methods:

  • Utilized NLP techniques to create an objective classification model for CBCA.
  • Employed data augmentation and dual contrastive learning to fine-tune the RoBERTa language model.
  • Applied model-based optimization for hyper-parameter tuning to maximize classification performance.

Main Results:

  • Achieved an 8.5% improvement in macro F1 score over human classification.
  • Demonstrated a 24% improvement in macro F1 score and a 13% increase in accuracy compared to previous benchmarks.
  • The model effectively reduced human subjectivity in statement analysis.

Conclusions:

  • The proposed NLP model offers a more objective and reliable method for evaluating victim statement credibility.
  • This advancement has significant implications for legal proceedings and criminal investigations.
  • Reducing subjectivity enhances verdict accuracy and supports the delivery of justice.