Are you speaking my languages? On spoken language adherence in multimodal LLMs
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Computer Science > Computation and Language
Title:Are you speaking my languages? On spoken language adherence in multimodal LLMs
Abstract:While Large Language Model (LLM) based Automatic Speech Recognition (ASR) enables seamless multilingual use, models often misidentify the output language, compromising transcription fidelity and downstream application quality. To preserve flexibility and code-switching capabilities, we propose a soft prompting approach that hints at potential spoken languages without strictly constraining the output. We formally define this challenge as a lack of language adherence, introduce a novel metric to quantify violations, and evaluate three mitigation strategies: (1) zero-shot prompting for robust guidance under uncertainty, (2) supervised fine-tuning (SFT) to improve prompt adherence, and (3) Chain-of-Thought (CoT) reasoning to enforce adherence during decoding. We present a comparative analysis of these methods across multiple languages, evaluating effectiveness in reducing the language violation while maintaining overall ASR performance. Finally, we discuss trade-offs to guide strategy selection under various compute constraints.
| Comments: | 7 pages, 3 tables in the main body |
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2606.17281 [cs.CL] |
| (or arXiv:2606.17281v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.17281
arXiv-issued DOI via DataCite (pending registration)
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