Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges
📰 ArXiv cs.AI
Comparative analysis of embedding-based and generative models for LLM-driven document classification in geoscience technical documents
Action Steps
- Evaluate the performance of embedding-based models for document classification
- Compare the results with generative Vision-Language Models (VLMs) like Qwen2.5-VL
- Investigate the impact of Chain-of-Thought (CoT) prompting on zero-shot accuracy
- Analyze the trade-offs between model accuracy, stability, and computational cost
Who Needs to Know This
AI engineers and researchers on a team benefit from this study as it provides insights into the trade-offs between model accuracy, stability, and computational cost, while data scientists can apply these findings to improve document classification tasks
Key Insight
💡 Generative Vision-Language Models with Chain-of-Thought prompting outperform embedding-based models in document classification tasks
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💡 Generative VLMs achieve 82% zero-shot accuracy for document classification with CoT prompting
Key Takeaways
Comparative analysis of embedding-based and generative models for LLM-driven document classification in geoscience technical documents
Full Article
Title: Evaluation of Embedding-Based and Generative Methods for LLM-Driven Document Classification: Opportunities and Challenges
Abstract:
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-
Abstract:
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-
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