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Lu Lu

4 papers hereh-index 343 citations13 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL4
same name
  • Lu Lu — 11 papers, h 7
  • Lu Lu — 10 papers, h 7
  • Lu Lu — 8 papers, h 10
  • Lu Lu — 7 papers, h 6
  • Lu Lu — 5 papers, h 2
  • Lu Lu — 4 papers, h 0

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators

4 papers

cs.CL2025

MedBench v4: A Robust and Scalable Benchmark for Evaluating Chinese Medical Language Models, Multimodal Models, and Intelligent Agents

Jinru Ding, Lu Lu, Chao Ding +15

Recent advances in medical large language models (LLMs), multimodal models, and agents demand evaluation frameworks that reflect real clinical workflows and safety constraints. We…

cs.CL2025

TCM-5CEval: Extended Deep Evaluation Benchmark for LLM's Comprehensive Clinical Research Competence in Traditional Chinese Medicine

Tianai Huang, Jiayuan Chen, Lu Lu +6

Large language models (LLMs) have demonstrated exceptional capabilities in general domains, yet their application in highly specialized and culturally-rich fields like Traditional…

cs.CL2025

Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies

Luyi Jiang, Jiayuan Chen, Lu Lu +4

The evaluation and improvement of medical large language models (LLMs) are critical for their real-world deployment, particularly in ensuring accuracy, safety, and ethical alignmen…

cs.CL2025

TCM-3CEval: A Triaxial Benchmark for Assessing Responses from Large Language Models in Traditional Chinese Medicine

Tianai Huang, Lu Lu, Jiayuan Chen +5

Large language models (LLMs) excel in various NLP tasks and modern medicine, but their evaluation in traditional Chinese medicine (TCM) is underexplored. To address this, we introd…

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