arXiv:2505.19937v3 Announce Type: replace Abstract: Large Language Models (LLMs) are extended into Speech-LLMs, and the quality of the audio--text alignment they learn affects most downstream Spoken Language Understanding (SLU) behavior. Yet despite a growth of fusion strategies, there is no standard way to measure how well a Speech-LLM internally binds audio frames to text tokens. We introduce ALAS (Automatic Latent Alignment Score), a model and task-agnostic metric that probes the LLM's per-layer hidden states, scoring the cross-modal cosine similarity between audio and text representations
The rapid development and integration of Large Language Models with speech capabilities necessitate robust evaluation metrics to ensure their reliability and performance for downstream applications.
A standardized, model-agnostic metric for audio-text alignment is crucial for advancing Speech-LLM development, enabling better model comparison, and accelerating innovation in spoken language understanding.
The introduction of ALAS provides a universal tool for objectively measuring the internal alignment quality of Speech-LLMs, moving beyond anecdotal or task-specific evaluations.
- · AI researchers and developers
- · Speech-LLM companies
- · Spoken Language Understanding applications
- · Proprietary, opaque alignment evaluation methods
- · Inefficient Speech-LLM development processes
ALAS provides a common benchmark for comparing different Speech-LLM architectures and training methodologies.
Improved alignment metrics lead to more accurate and robust Speech-LLMs, accelerating their adoption across various industries.
Standardized evaluation could foster greater collaboration and interoperability in the Speech-LLM ecosystem, potentially leading to more specialized and efficient models.
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