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Towards Adaptive Mechanism Activation in Language Agent
Summary
This research paper introduces ALAMA, a novel method for enhancing Language Agents (LAs) by enabling adaptive mechanism activation. ALAMA uses a unified framework (UniAct) to integrate various mechanisms like reasoning and planning, and employs self-exploration to generate training data, optimizing mechanism selection based on task characteristics. The authors demonstrate ALAMA's effectiveness through experiments on mathematical and knowledge-intensive reasoning tasks, showcasing superior performance compared to existing baselines. Their approach significantly improves efficiency by reducing reliance on expert-curated data, making it more scalable and practical. Future work includes exploring concurrent mechanism activation and further analyzing the effects of mixing data from different mechanisms.
原文链接：https://arxiv.org/abs/2412.00722
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