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

• URL: http://arxiv.org/abs/2603.20433v1
• Date: Fri, 20 Mar 2026 19:03:29 GMT
• Title: ALICE: A Multifaceted Evaluation Framework of Large Audio-Language Models' In-Context Learning Ability
• Abstract summary: Large Audio-Language Models (LALMs) have been shown to exhibit degraded instruction-following capabilities.<n>We present ALICE, a framework that progressively reduces textual guidance to evaluate LALMs' in-context learning ability under audio conditioning.
• Score: 35.45844102229014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Evaluating six LALMs across four audio understanding tasks under two output constraint categories, we uncover a consistent asymmetry across all stages and LALMs: in-context demonstrations reliably improve format compliance but fail to improve, and often degrade, the core task performance. This suggests that LALMs can glean surface-level formatting patterns from demonstrations but may struggle to leverage cross-modal semantic grounding to reliably infer task objectives from audio-conditioned examples, highlighting potential limitations in current cross-modal integration.

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