arXiv (All)
open access
LingLanMedBench: A Unified Benchmark for Evaluating Large Language Models in Traditional Chinese Medicine
Reliable evaluation of large language models in traditional Chinese medicine remains limited because existing benchmarks mainly focus on examination-style question answering or isolated generative tasks. We developed LingLanMedBench, a unified benchmark comprising 25,620 expert-verified items across
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
- The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.#653520
- Cross-Lingual Clinical Annotation Projection as Constrained Text Generation: A Six-Language Study#998979
- Large language models show promising performance for some systematic review tasks but call for cautious implementation: a systematic review#364033
- A systematic comparison of ChatGPT and DeepSeek for guideline-based question answering in obstetric anesthesia.#305334
- Bactrainus: Optimizing Large Language Models for Multi-hop Complex Question Answering Tasks#629756
Record · ID 1344500
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