Cascaded systems remain the most reliable for speech translation overall, but recent SpeechLLMs match or outperform them in many conditions while standalone speech models lag.
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Hybrid DP with LLM or NER preprocessing significantly improves the privacy-utility trade-off for Dutch clinical note de-identification compared to standalone DP.
ReMedi boosts LLM performance on EHR clinical predictions by up to 19.9% F1 through ground-truth-guided rationale regeneration and fine-tuning.
LLM-generated ML pipelines show higher bias (87.7% sensitive attributes) than conditional statements (59.2%), indicating that simple if-statement tests underestimate bias risk in practical code generation.
citing papers explorer
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Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs
Cascaded systems remain the most reliable for speech translation overall, but recent SpeechLLMs match or outperform them in many conditions while standalone speech models lag.
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Differentially Private De-identification of Dutch Clinical Notes: A Comparative Evaluation
Hybrid DP with LLM or NER preprocessing significantly improves the privacy-utility trade-off for Dutch clinical note de-identification compared to standalone DP.
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ReMedi: Reasoner for Medical Clinical Prediction
ReMedi boosts LLM performance on EHR clinical predictions by up to 19.9% F1 through ground-truth-guided rationale regeneration and fine-tuning.
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From If-Statements to ML Pipelines: Revisiting Bias in Code-Generation
LLM-generated ML pipelines show higher bias (87.7% sensitive attributes) than conditional statements (59.2%), indicating that simple if-statement tests underestimate bias risk in practical code generation.