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For optimal performance, AI models require top-notch data. Obtaining and organizing this data may be quite a challenge, unfortunately. There is a risk that publicly available datasets must be more adequate, too broad, or tainted to be useful for some purposes. It can be challenging to find domain experts, which is a problem for many datasets. There is a need for Golden Datasets and Frontier Benchmarking in a world where AI propels economic growth and promotes scientific research. The goal of iteratively testing the model’s efficacy on different use scenarios is to Data for Training: If someone want to boost the model’s performance with RLHF and fine-tuning Before releasing LLMs into the wild, it is important to assess and predict their safety by red-teaming.
Publicly available benchmarks that are either too vague or inaccurate to be of any use to real product creators need to be made, and the majority of data requires domain knowledge, which can be difficult to collect and curate. Advanced data is essential to deploy and scale AI safely. Nevertheless, gathering this information is no picnic. Collecting and curating domain knowledge (e.g., medicine, biology, physics, finance, etc.) for most frontier data can be challenging. The publicly available benchmarks, such as MMLU, GPQA, MATH, etc., are polluted and overly simplistic to be of any use to the people who construct products and models.
Meet Sepal AI, a data development tool that lets you create valuable datasets through curation. Sepal offers advanced data and tools to promote ethical AI development. By responsibly developing AI, Sepal AI aims to expand human knowledge and capacities.
Responsible behaviors are highly valued by Sepal AI, which acknowledges the ethical considerations surrounding AI development. The platform helps build AI models that are good for society, impartial, and fair by giving resources for making high-quality data. By incorporating human expertise, synthetic data augmentation, data generating tools, and stringent quality control, Sepal AI makes it easy to oversee the creation of reliable datasets.
Sepal AI is involved in the following engagements:
Molecular and Cellular Biology Benchmark: A novel approach to comparing models’ complicated thinking abilities. It was developed by a group of highly regarded American PhD scientists.
Finance Q&A + SQL Eval: A Golden Dataset to evaluate an AI agent’s database querying skills and generate responses to complex finance inquiries comparable to human experts.
Uplift Trials & Human Baselining: Comprehensive End-to-End Support for Safe, In-Person Model Evaluations.
In Conclusion
Sepal AI solves this data shortage by enabling individuals and companies to develop meaningful datasets. Sepal AI provides an all-encompassing method for data development by integrating tools for data generation, synthetic data augmentation, stringent quality control, and an expert network.
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