arXiv (All)
open access
PCQC: Privileged Counterfactual Question Credit for Multi-Turn Medical Dialogue
Large language models (LLMs) have made substantial progress on medical question-answering, yet effective medical dialogue also requires learning to ask questions that uncover relevant patient information. To train such dialogue policies, a common pipeline combines supervised fine-tuning with reinfor
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.#218845
- Speech-based Assessment of Relapse Risk in People With Psychosis#868925
- NLP Headache Speech (NLPH-SPEECH): a Cross-sectional Study on Natural Language Processing Analysing Spoken Monologues by Headache Patients#982208
- Template-assisted Contrastive Learning of Task-oriented Dialogue Sentence Embeddings#139052
- Candidate-Expanding Routing with Permutation-Stabilized Experts for Mixed-Format Medical VQA#820953
Record · ID 1064531
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive