Common Sense Reasoning On Arc Challenge

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title
GPT-4 (few-shot, k=25)	96.4	GPT-4 Technical Report
PaLM 2 (few-shot, CoT, SC)	95.1	PaLM 2 Technical Report
Shivaay (4B, few-shot, k=8)	91.04	-
StupidLLM	91.03	-
Claude 2 (few-shot, k=5)	91	Model Card and Evaluations for Claude Models
Claude 1.3 (few-shot, k=5)	90	Model Card and Evaluations for Claude Models
PaLM 540B (Self Improvement, Self Consistency)	89.8	Large Language Models Can Self-Improve
PaLM 540B (Self Consistency)	88.7	Large Language Models Can Self-Improve
PaLM 540B (Self Improvement, CoT Prompting)	88.3	Large Language Models Can Self-Improve
PaLM 540B (Self Improvement, Standard-Prompting)	87.2	Large Language Models Can Self-Improve
PaLM 540B (Standard-Prompting)	87.1	Large Language Models Can Self-Improve
ST-MoE-32B 269B (fine-tuned)	86.5	ST-MoE: Designing Stable and Transferable Sparse Expert Models
Claude Instant 1.1 (few-shot, k=5)	85.7	Model Card and Evaluations for Claude Models
PaLM 540B (CoT Prompting)	85.2	Large Language Models Can Self-Improve
GPT-3.5 (few-shot, k=25)	85.2	GPT-4 Technical Report
LLaMA 3 8B + MoSLoRA (fine-tuned)	81.5	Mixture-of-Subspaces in Low-Rank Adaptation
LLaMA-3 8B + MixLoRA	79.9	MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
LLaMA-2 13B + MixLoRA	69.9	MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
PaLM 2-L (1-shot)	69.2	PaLM 2 Technical Report
GAL 120B (zero-shot)	67.9	Galactica: A Large Language Model for Science

0 of 54 row(s) selected.

Accuracy

Performance results of various models on this benchmark

		Paper Title
GPT-4 (few-shot, k=25)	96.4	GPT-4 Technical Report
PaLM 2 (few-shot, CoT, SC)	95.1	PaLM 2 Technical Report
Shivaay (4B, few-shot, k=8)	91.04	-
StupidLLM	91.03	-
Claude 2 (few-shot, k=5)	91	Model Card and Evaluations for Claude Models
Claude 1.3 (few-shot, k=5)	90	Model Card and Evaluations for Claude Models
PaLM 540B (Self Improvement, Self Consistency)	89.8	Large Language Models Can Self-Improve
PaLM 540B (Self Consistency)	88.7	Large Language Models Can Self-Improve
PaLM 540B (Self Improvement, CoT Prompting)	88.3	Large Language Models Can Self-Improve
PaLM 540B (Self Improvement, Standard-Prompting)	87.2	Large Language Models Can Self-Improve
PaLM 540B (Standard-Prompting)	87.1	Large Language Models Can Self-Improve
ST-MoE-32B 269B (fine-tuned)	86.5	ST-MoE: Designing Stable and Transferable Sparse Expert Models
Claude Instant 1.1 (few-shot, k=5)	85.7	Model Card and Evaluations for Claude Models
PaLM 540B (CoT Prompting)	85.2	Large Language Models Can Self-Improve
GPT-3.5 (few-shot, k=25)	85.2	GPT-4 Technical Report
LLaMA 3 8B + MoSLoRA (fine-tuned)	81.5	Mixture-of-Subspaces in Low-Rank Adaptation
LLaMA-3 8B + MixLoRA	79.9	MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
LLaMA-2 13B + MixLoRA	69.9	MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
PaLM 2-L (1-shot)	69.2	PaLM 2 Technical Report
GAL 120B (zero-shot)	67.9	Galactica: A Large Language Model for Science

0 of 54 row(s) selected.