ComplexBench ↗0.8%
GPT-4-1106 · ComplexBench · DRFR · all constraints · RAL evaluation ↗
OpenAI / MODEL EVIDENCE
GPT-4-1106
2 benchmarks · 9 observations · 2 sources
Scores at a glance
2 benchmarks · large dot: GPT-4-1106; small dots: closest models where reported
CRAFT-MD ↗48.6%
GPT-4-1106 · CRAFT-MD · Free response · Vignette accuracy · combined dataset ↗
7 more reported results
26.4% · GPT-4-1106 · CRAFT-MD · Free response · Multi-turn conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
13.3% · GPT-4-1106 · CRAFT-MD · Free response · Single-turn conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
27.2% · GPT-4-1106 · CRAFT-MD · Free response · Summarized conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
82.1% · GPT-4-1106 · CRAFT-MD · Four-choice MCQ · Vignette accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
62.7% · GPT-4-1106 · CRAFT-MD · Four-choice MCQ · Multi-turn conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
52% · GPT-4-1106 · CRAFT-MD · Four-choice MCQ · Single-turn conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
67.1% · GPT-4-1106 · CRAFT-MD · Four-choice MCQ · Summarized conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
There are not enough shared scored settings to identify close comparisons reliably.
Sources
Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.
- Benchmarking Complex Instruction-Following with Multiple Constraints Composition — published result table ↗
All 14 authors
Bosi Wen, Pei Ke, Xiaotao Gu, Lindong Wu, Hao Huang, Jinfeng Zhou, Wenchuang Li, Binxin Hu, Wendy Gao, Jiaxin Xu, Yiming Liu, Jie Tang, Hongning Wang, Minlie Huang
- An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗
All 12 authors
Shreya Johri, Jaehwan Jeong, Benjamin A. Tran, Daniel I. Schlessinger, Shannon Wongvibulsin, Leandra A. Barnes, Hong-Yu Zhou, Zhuo Ran Cai, Eliezer M. Van Allen, David Kim, Roxana Daneshjou, Pranav Rajpurkar
Names used in source tables
GPT-4-1106