ALICE: A Multifaceted Evaluation Framework of Large Audio-Language Models' In-Context Learning Ability

ArXi:2603.20433v1 Announce Type: cross While Large Audio-Language Models (LALMs) have been shown to exhibit degraded instruction-following capabilities, their ability to infer task patterns from in-context examples under audio conditioning remains unstudied. To address this gap, we present ALICE, a three-stage framework that progressively reduces textual guidance to systematically evaluate LALMs' in-context learning ability under audio conditioning.