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cs.CL2025
Do Audio LLMs Really LISTEN, or Just Transcribe? Measuring Lexical vs. Acoustic Emotion Cues Reliance
Jingyi Chen, Zhimeng Guo, Jiyun Chun +3
Understanding emotion from speech requires sensitivity to both lexical and acoustic cues. However, it remains unclear whether large audio language models (LALMs) genuinely process…
cs.CL2025
Prompt and circumstance: A word-by-word LLM prompting approach to interlinear glossing for low-resource languages
Micha Elsner, David Liu
Partly automated creation of interlinear glossed text (IGT) has the potential to assist in linguistic documentation. We argue that LLMs can make this process more accessible to lin…
cs.CL2024
Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem
Sara Court, Micha Elsner
This work investigates the in-context learning abilities of pretrained large language models (LLMs) when instructed to translate text from a low-resource language into a high-resou…