Entity Binding Failures in Speech LLM Reasoning: Diagnosis and Chain-of-Thought Intervention

Abstract:Speech Large Language Models (SLLMs) underperform their text counterparts on complex reasoning. We reveal that this modality gap is not a uniform cognitive deficit. Evaluating three diverse SLLMs, we show speech-to-text (S2T) matches or exceeds text-to-text (T2T) on spatial, syntactic, and factual tasks. However, on logical tasks requiring entity tracking, S2T accuracy collapses to chance. We diagnose this localized degradation as an entity binding failure: continuous speech features cause models to lose precise entity-property associations during implicit reasoning. To resolve this, we propose Entity-Aware Chain-of-Thought (EA-CoT), forcing SLLMs to explicitly enumerate entities and bind them to claims before reasoning. Strikingly, EA-CoT bridges the gap, even when spoken names are misrecognized, yielding up to a 24.4% absolute accuracy improvement. Ablations confirm these gains stem entirely from explicit semantic binding, reframing the gap as a resolvable bottleneck.

Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)

arXiv:2606.04474 [cs.CL]

(or arXiv:2606.04474v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.04474 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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