SPARE fine-tuning keeps audio-language models grounded while explaining reasoning
burkov · x · 2026-11-02
Audio-language models answer complex questions about sound, but asking them to explain their reasoning can hurt accuracy: as explanations grow, models rely on their own generated text instead of the audio, producing convincing but ungrounded answers.
SPARE is a fine-tuning method that gives the model an early representation of the intended conclusion before reasoning. During training, a special token is encouraged to encode the conclusion's meaning, using a sentence-embedding model as target. Tested with SALMONN 13B on 160,000 audio examples and evaluated on MMAU and MMAR benchmarks, it aims to keep audio evidence in focus without added inference cost or latency.
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