MT-NLG · Accuracy ↗
3 more reported results
82.51% · MT-NLG · Accuracy · Epoch AI · BoolQ · public result data ↗
84.83% · MT-NLG · Accuracy · Epoch AI · BoolQ · public result data ↗
78.2% · MT-NLG · Accuracy · Epoch AI · BoolQ · public result data ↗
NVIDIA / MODEL EVIDENCE
1 benchmarks · 4 observations · 2 sources
1 benchmarks · large dot: MT-NLG; small dots: closest models where reported
MT-NLG · Accuracy ↗
82.51% · MT-NLG · Accuracy · Epoch AI · BoolQ · public result data ↗
84.83% · MT-NLG · Accuracy · Epoch AI · BoolQ · public result data ↗
78.2% · MT-NLG · Accuracy · Epoch AI · BoolQ · public result data ↗
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
There are not enough shared scored settings to identify close comparisons reliably.
Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.
Almazrouei, Ebtesam, Alobeidli, Hamza, Alshamsi, Abdulaziz, Cappelli, Alessandro, Cojocaru, Ruxandra, Debbah, Mérouane, Goffinet, Étienne, Hesslow, Daniel, Launay, Julien, Malartic, Quentin, Mazzotta, Daniele, Noune, Badreddine, Pannier, Baptiste, Penedo, Guilherme
Smith, Shaden, Patwary, Mostofa, Norick, Brandon, LeGresley, Patrick, Rajbhandari, Samyam, Casper, Jared, Liu, Zhun, Prabhumoye, Shrimai, Zerveas, George, Korthikanti, Vijay, Zhang, Elton, Child, Rewon, Aminabadi, Reza Yazdani, Bernauer, Julie, Song, Xia, Shoeybi, Mohammad, He, Yuxiong, Houston, Michael, Tiwary, Saurabh, Catanzaro, Bryan
MT-NLG