Preserve to Expand: Source-Aware Adaptation and Model Merging for Multilingual LLM-Based ASR
Paper recorded by Signals 4 on 2026-10-26 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-26 on arXiv · recorded by Signals 4 on 2026-10-27
Category: cs.CL · 自然语言处理 · first seen 2026-10-27
Abstract
Expanding multilingual ASR systems to new languages is challenging when adaptation must preserve previously supported languages without replaying source-language training data. We study this problem in connector-based Speech LLMs, where a trainable connector maps speech representations into a frozen large language model. We evaluate target-update construction and merging strategies for adapting th
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Cite this page: Preserve to Expand: Source-Aware Adaptation and Model Merging for Multilingual LLM-Based ASR: the #134 most recent of 660 cs.CL papers we have recorded (as of 2026-10-26). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/preserve-to-expand-source-aware-adaptation-and-model-merging-for-multilingual-ll.html
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