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What are the key differences between the new Stanford Helios Helm lite V100 benchmark and its predecessors in terms of performance and implementation?

**Simplified framework**: HELM Lite simplifies its predecessor, HELM Classic, by using only one random seed, reducing computational resources required, and focusing on capabilities rather than safety.

**Continuous updates**: HELM Lite is a living benchmark, designed to be continuously updated with new scenarios, metrics, and models, making it a dynamic evaluation framework.

**Modular design**: Unlike other benchmarks, HELM Lite has a modular design, allowing for easy additions and modifications to its framework.

**No robustness and fairness metrics**: Unlike its predecessor, HELM Lite does not measure robustness and fairness, as these metrics are well-correlated with accuracy.

**Focused on capabilities**: HELM Lite evaluates language models based on their capabilities, rather than safety, making it a unique benchmark in the field.

**Partnership with MLCommons**: HELM Lite is developed in partnership with MLCommons' AI safety working group, ensuring a collaborative approach to language model evaluation.

**In-context learning focus**: HELM Lite focuses on evaluating language models using in-context learning, a key area of interest in natural language processing.

**30 models and 10 scenarios at launch**: The new benchmark includes a leaderboard with 30 models and 10 scenarios, showcasing its comprehensive approach to evaluation.

**Multidisciplinary tasks**: HELM Lite includes tasks that require deliberate reasoning and college-level subject knowledge, making it a challenging benchmark for language models.

**Publicly accessible data and analysis**: All data and analysis are freely accessible on the website, allowing researchers to explore and study the results in-depth.

**Holistic evaluation**: HELM Lite provides a broad coverage of language models, recognizing incompleteness and defining a taxonomy over the scenarios evaluated.

**Multimetric measurements**: The benchmark uses multiple metrics to measure language models' performance, providing a more comprehensive evaluation than traditional benchmarks.

**Connection to AI safety**: HELM Lite is part of a broader effort to develop safety benchmarks for large language models, highlighting its significance in the field of AI safety.

**Community-driven development**: The benchmark is designed to be a living benchmark, continuously updated with contributions from the community, ensuring its relevance and effectiveness.

**CRFM involvement**: The Center for Research on Foundation Models (CRFM) at Stanford University is actively involved in the development of HELM Lite, ensuring its academic rigor and expertise.

**Python package availability**: The crfmhelm Python package contains code used in the Holistic Evaluation of Language Models project, making it easily accessible for researchers and developers.

**New categories added**: HELM Lite includes new categories such as medicine (MedQA), law (LegalBench), and machine translation (WMT14), expanding its scope and relevance.

**Subset of the original HELM**: HELM Lite is a subset of the original HELM benchmark, with some categories added, making it a more focused and efficient evaluation framework.

**Releases and contributors**: The benchmark has multiple releases, with contributors from the community, ensuring its continuous development and improvement.

**GitHub repository**: The crfmhelm Python package is hosted on GitHub, making it easily accessible for researchers and developers to explore and contribute to.

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