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Common Sense Reasoning On Winogrande
Common Sense Reasoning On Winogrande
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Accuracy
Paper Title
ST-MoE-32B 269B (fine-tuned)
96.1
ST-MoE: Designing Stable and Transferable Sparse Expert Models
Unicorn 11B (fine-tuned)
91.3
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark
CompassMTL 567M with Tailor
90.5
Task Compass: Scaling Multi-task Pre-training with Task Prefix
CompassMTL 567M
89.6
Task Compass: Scaling Multi-task Pre-training with Task Prefix
UnifiedQA 11B (fine-tuned)
89.4
UnifiedQA: Crossing Format Boundaries With a Single QA System
Claude 3 Opus (5-shot)
88.5
The Claude 3 Model Family: Opus, Sonnet, Haiku
GPT-4 (5-shot)
87.5
GPT-4 Technical Report
ExDeBERTa 567M
87
Task Compass: Scaling Multi-task Pre-training with Task Prefix
LLaMA-2 13B + MixLoRA
86.3
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
LLaMA3 8B+MoSLoRA
85.8
Mixture-of-Subspaces in Low-Rank Adaptation
PaLM 2-L (1-shot)
83.0
PaLM 2 Technical Report
LLaMA-3 8B + MixLoRA
82.1
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
ST-MoE-L 4.1B (fine-tuned)
81.7
ST-MoE: Designing Stable and Transferable Sparse Expert Models
GPT-3.5 (5-shot)
81.6
GPT-4 Technical Report
PaLM 540B (0-shot)
81.1
PaLM: Scaling Language Modeling with Pathways
Camelidae-8×34B
80.9
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks
PaLM 2-M (1-shot)
79.2
PaLM 2 Technical Report
RoBERTa-Winogrande 355M (fine-tuned)
79.1
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
PaLM 2-S (1-shot)
77.9
PaLM 2 Technical Report
Mixtral 8x7B (0-shot)
77.2
Mixtral of Experts
0 of 77 row(s) selected.
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Common Sense Reasoning On Winogrande | SOTA | HyperAI