ALAMA introduces adaptive mechanism activation for language agents, enabling them to automatically select optimal solution strategies for different tasks through self-exploration, rather than relying on fixed mechanisms or predefined sequences.
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https://arxiv.org/abs/2412.00722
🤔 Original Problem:
Current language agents use fixed mechanisms or predefined sequences for tasks, limiting their adaptability and performance across diverse scenarios. Oracle-based mechanism selection shows potential 15% improvement over fixed approaches, highlighting the need for adaptive solutions.
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🛠️ Solution in this Paper:
→ ALAMA introduces UniAct framework that unifies different mechanisms (Reason, Plan, Memory, Reflection, External-Augmentation) via standardized actions
→ Implements self-exploration to generate diverse solution trajectories without relying on expert models
→ Uses Implicit Mechanism Activation Optimization (IMAO) for basic capabilities
→ Employs Mechanism Activation Adaptability Optimization (MAAO) for enhanced mechanism selection
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💡 Key Insights:
→ Only 42.61% tasks can be solved by all fixed mechanisms, showing mechanism sensitivity matters
→ Oracle mechanism activation achieves 96.89% success rate, indicating high potential ceiling
→ Mixed mechanism training outperforms single mechanism approaches
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📊 Results:
→ ALAMA surpasses baselines by 3.95 points on NumGLUE and 2.3 points on SVAMP
→ Self-Adapt Consistency improves performance by 2.35 points on GSM8K
→ Outperforms GPT-3.5-turbo average on held-in tasks
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