Are you speaking my languages? On spoken language adherence in multimodal LLMs
This paper addresses the issue of language misidentification in LLM-based ASR, proposing a soft prompting approach, defining a language adherence metric, and evaluating three mitigation strategies: zero-shot prompting, supervised fine-tuning, and chain-of-thought reasoning.
[2606.17281] Are you speaking my languages? On spoken language adherence in multimodal LLMs
[Submitted on 15 Jun 2026]
Title:Are you speaking my languages? On spoken language adherence in multimodal LLMs
View a PDF of the paper titled Are you speaking my languages? On spoken language adherence in multimodal LLMs, by Hyungwon Kim and 4 other authors
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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)
Submission history
From: Hyungwon Kim [view email] [v1] Mon, 15 Jun 2026 20:44:42 UTC (192 KB)
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