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The authors identify language-selective hidden-state dimensions in pretrained speech encoders and aggregate their frame-level activations to detect code-switching (CS) directly from audio. Using a small tuning set, the Whisper-small encoder achieved a mean F1 of 0.798 across 14 language pairs on CS-FLEURS, outperforming a CNN (0.776) and a HuBERT linear baseline (0.751).
The paper studies how fine-tuning conversational LLMs on curated subsets of preference datasets that express specific values (e.g., helpfulness, honesty) changes model behaviour beyond the targeted traits. By measuring expression of other values, model safety, anthropomorphic language, and QA performance, the authors find that (i) inducing a value can elicit related or sometimes contrasting values, (ii) inducing positive values tends to increase safety, and (iii) all value inductions increase anthropomorphic, validating and sycophantic language.
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