Q-learning helps LLMs choose demonstrations that cover more ground in classification tasks.
This paper introduces Relevance-Diversity Enhanced Selection (RDES), a reinforcement learning framework that optimizes demonstration selection for text classification using LLMs. It balances both diversity and relevance in selecting examples, significantly improving few-shot learning performance compared to traditional methods.
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https://arxiv.org/abs/2412.03966
🤔 Original Problem:
Traditional demonstration selection methods for LLMs prioritize similarity over diversity, leading to biased representations and suboptimal performance in few-shot learning scenarios. This limits the model's ability to generalize across different classification tasks.
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🔧 Solution in this Paper:
→ RDES employs Q-learning to dynamically identify demonstrations that maximize both diversity and relevance to classification objectives.
→ The framework calculates diversity scores based on label distribution among selected demonstrations.
→ It integrates Chain-of-Thought reasoning to enhance the model's predictive capabilities.
→ The system uses reinforcement learning to adaptively refine demonstration selection based on performance feedback.
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💡 Key Insights:
→ Diversity in demonstration selection is crucial for model generalization
→ Q-learning framework effectively balances relevance and diversity
→ Chain-of-Thought reasoning significantly improves classification accuracy
→ Adaptive selection outperforms fixed strategies
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📊 Results:
→ Outperformed 10 established baselines across 4 benchmark datasets
→ Tested successfully on 12 closed-source and open-source LLMs
→ Achieved significant enhancement in classification accuracy
→ Demonstrated robust performance across diverse classification tasks
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