Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges
Summary: arXiv:2604.04997v1 Announce Type: cross
Abstract
This work presents a comparative analysis of embedding-based and generative models for classifying geoscience technical documents. Using a multi-disciplinary benchmark dataset, we evaluated the trade-offs between model accuracy, stability, and computational cost. We find that generative Vision-Language Models (VLMs) like Qwen2.5-VL, enhanced with Chain-of-Thought (CoT) prompting, achieve superior zero-shot accuracy (82%) compared to state-of-the-art multimodal embedding models like QQMM (63%). We also demonstrate that while supervised fine-tuning (SFT) can improve VLM performance, it is sensitive to training data imbalance.
Introduction
Document classification is a critical task in various domains, including geoscience. Recent advancements in machine learning, particularly in Large Language Models (LLMs), have opened new avenues for enhancing classification accuracy. This article discusses the strengths and weaknesses of embedding-based models and generative models, providing insights into their applicability in document classification.
Embedding-Based Models
Embedding-based models have been the cornerstone of natural language processing for years. They represent text as fixed-size vectors in a high-dimensional space, facilitating various downstream tasks such as classification. Key features include:
- Proven Accuracy: These models have demonstrated significant accuracy in classification tasks, especially when fine-tuned on domain-specific data.
- Computational Efficiency: They often require less computational resources compared to generative models, making them suitable for real-time applications.
- Stability: Embedding models typically offer stable performance across diverse datasets.
Generative Models
Generative models, particularly Vision-Language Models (VLMs), have recently gained attention due to their remarkable capabilities in understanding and generating text. The analysis highlights the following aspects:
- Zero-Shot Learning: Models like Qwen2.5-VL have shown impressive zero-shot accuracy, achieving 82% in document classification tasks without task-specific training.
- Chain-of-Thought Prompting: Enhancements through CoT prompting have significantly improved the output quality and reasoning capabilities of generative models.
- Adaptability: These models can be fine-tuned for specific applications, though they may struggle with data imbalances during training.
Comparative Analysis
The comparative study reveals crucial insights into the trade-offs between embedding-based and generative methods:
- Model Accuracy: Generative models generally outperform embedding models in zero-shot scenarios but may require more computational resources.
- Training Sensitivity: While SFT improves performance, it is susceptible to training data imbalance, affecting the overall robustness of the model.
- Cost-Effectiveness: Embedding models are often more cost-effective, especially for applications requiring quick inference times.
Conclusion
The evaluation of embedding-based and generative methods for document classification illustrates the evolving landscape of machine learning models. While generative models like Qwen2.5-VL exhibit superior zero-shot performance, embedding-based models remain a reliable choice for many applications. Future research should focus on addressing the challenges associated with data imbalance and exploring hybrid approaches that leverage the strengths of both methodologies.