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arXiv · 2604.04997

Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges

Rong Lu·Hao Liu·Song Hou
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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.

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Rong Lu, Hao Liu, Song Hou. 2026-04-05. Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges. https://doi.org/10.1190/image2025-w11-03.1

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