HellaSwag

Overview edit

Variants: HellaSwag, HellaSwag (10-Shot), HellaSwag TR

Associated Benchmarks

This dataset is used in 4 benchmarks:

• Question Answering - Metrics: Accuracy
• Text Generation - Metrics: Accuracy, acc
• parameter-efficient fine-tuning - Metrics: Accuracy (% )
• Sentence Completion - Metrics: Accuracy

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments (2024) -
• GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs (2024) -
• Mixture-of-Subspaces in Low-Rank Adaptation (2024) -
• MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts (2024) -
• DoRA: Weight-Decomposed Low-Rank Adaptation (2024) -
• Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks (2024) -
• LLM in a flash: Efficient Large Language Model Inference with Limited Memory (2023) -
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces (2023) -
• The Falcon Series of Open Language Models (2023) -
• Mistral 7B (2023) -
• Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning (2023) -
• Llama 2: Open Foundation and Fine-Tuned Chat Models (2023) -
• Stay on topic with Classifier-Free Guidance (2023) -
• The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning (2023) -
• PaLM 2 Technical Report (2023) -
• LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions (2023) -
• BloombergGPT: A Large Language Model for Finance (2023) -
• GPT-4 Technical Report (2023) -
• LLaMA: Open and Efficient Foundation Language Models (2023) -
• Exploring the Benefits of Training Expert Language Models over Instruction Tuning (2023) -