hellaswag

**HellaSwag** is a **dataset for commonsense natural language inference (NLI) that asks the model to complete a sentence describing a physical situation or event** — constructed using Adversarial Filtering to ensure the correct ending is difficult for BERT-like models to guess based on distribution alone. **Task** - **Context**: "A woman is outside with a bucket and a dog. The dog is running around trying to avoid a bath. She..." - **Ending A**: "...rinses the bucket." - **Ending B**: "...grabs the dog and washes it." (Correct). - **Ending C**: "...gets in the bucket herself." **Why It Matters** - **ActivityNet**: Derived from video captions — focuses on grounded, temporal events. - **Adversarial**: Specifically designed to break BERT; endings that "sounded right" to BERT but were nonsensical to humans were generated as distractors. - **LLM Benchmark**: Remains a standard score reported for all new Foundation Models (GPT-3, LLaMA). **HellaSwag** is **predicting the next scene** — testing if the model understands how physical events and human actions typically unfold.