Small AI models can match their bigger siblings' smarts, with this paper's teaching tricks.
ZeroG introduces a knowledge distillation framework that reduces hallucinations in LLMs while maintaining high performance. It uses a teacher-student model architecture where a smaller model learns from a larger one, creating a distilled dataset for efficient, accurate responses.
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https://arxiv.org/abs/2411.05936
🤔 **Original Problem**:
Traditional document management systems struggle with hallucinations and high latency when processing complex documents. LLMs often produce unreliable responses when handling sensitive business data, making them unsuitable for enterprise use.
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🔧 **Solution in this Paper**:
→ ZeroG employs a black-box distillation approach where a smaller model replicates a larger teacher model's behavior without accessing intermediate features.
→ The system converts PowerPoint presentations into markdown format and uses graph databases for semantic management.
→ It implements MMR-based similarity search instead of traditional cosine similarity, improving retrieval accuracy by 12%.
→ The architecture uses Phi-3-mini as the student model and Qwen2-7b as the teacher model for generating QnA pairs.
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💡 **Key Insights**:
→ Knowledge distillation can effectively reduce hallucinations without compromising response quality
→ MMR search outperforms cosine similarity in document retrieval
→ Black-box distillation achieves better results than traditional RAG approaches
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📊 **Results**:
→ Accuracy improved from 73% to 87.5% compared to traditional RAG
→ Response latency reduced from 17 seconds to under 6 seconds
→ Comprehensibility increased from 91% to 97%
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