TalTech Systems Win Beyond Transcription Challenge with Robust Summarization of Doctor-Patient Conversations
TalTech's systems achieved first place in both tracks of the Beyond Transcription Challenge (BeTraC), generating SOAP notes directly from long doctor-patient conversation recordings without intermediate transcription. They adapted Voxtral Mini and Voxtral Small models using LoRA fine-tuning and DAPO reinforcement learning with the Open Medical Concept F1 metric as a reward. Independent evaluation found their submissions had the lowest hallucination rate among all entries.
Why it matters: This result shows that reinforcement learning with a concept-matching metric can improve factual reliability in medical summarization from speech, and that text-based fine-tuning can transfer effectively to speech input.
Full story at: arXiv Computation and Language ↗