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Towards Adaptive Mechanism Activation in Language Agent

arXiv 2412.00722 ↗First posted 1 Dec 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

Language Agent could be endowed with different mechanisms for autonomous task accomplishment. Current agents typically rely on fixed mechanisms or a set of mechanisms activated in a predefined order, limiting their adaptation to varied potential task solution structures. To this end, this paper proposes Adaptive Language Agent Mechanism Activation Learning with Self-Exploration (ALAMA), which focuses on optimizing mechanism activation adaptability without reliance on expert models. Initially, it builds a harmonized agent framework (UniAct) to Unify different mechanisms via Actions. Then it leverages a training-efficient optimization method based on self-exploration to enable the UniAct to adaptively activate the appropriate mechanisms according to the potential characteristics of the task. Experimental results demonstrate significant improvements in downstream agent tasks, affirming the effectiveness of our approach in facilitating more dynamic and context-sensitive mechanism activation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 43 of its 62 references that are in the map. First posted on arXiv 1 Dec 2024.

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References · 43

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