LoRA-LiteE makes chatbot preference tuning accessible by doing more with less computational power.
LoRA-LiteE combines lightweight models with ensemble learning to achieve GPT-4 level preference tuning using minimal computational resources.
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https://arxiv.org/abs/2411.09947
🤔 Original Problem:
Advanced chatbot preference tuning methods like RLHF require extensive computational resources and complex training pipelines, making them inaccessible for smaller organizations.
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🔧 Solution in this Paper:
→ LoRA-LiteE integrates Low-Rank Adaptation with ensemble learning to fine-tune lightweight chatbot models efficiently.
→ The framework processes 57,477 training samples containing user queries paired with model responses through systematic preprocessing and tokenization.
→ It employs parameter-efficient fine-tuning using LoRA, reducing trainable parameters while maintaining performance.
→ The final prediction combines outputs from multiple models using weighted averaging: 0.7 × Probability_gemma + 0.3 × Probability_llama.
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💡 Key Insights:
→ Parameter-efficient fine-tuning can match RLHF performance with less computation
→ Ensemble learning effectively bridges performance gap between small and large models
→ Resource constraints significantly impact larger models' performance gains
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📊 Results:
→ LoRA-LiteE achieved 80.2% accuracy, outperforming GPT-4 (78.3%)
→ Faster convergence than larger models in first 7 hours of training
→ Reduced computational footprint while maintaining competitive performance
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