Dataset Quality is Not Improving Much
The article discusses the lack of progress in creating high-quality datasets for AI, particularly for instruction-following tasks. The author highlights a few datasets they consider the most well-rounded, but expresses concern about the overall quality and innovation in this area.
Why it matters
The quality of datasets is crucial for the development of effective AI systems, and the lack of progress in this area is a concern for the industry.
Key Points
- 1Lack of breakthrough in dataset creation for AI, especially for instruction-following tasks
- 2The author highlights a few datasets they consider the best, but find them lacking in certain areas
- 3The author discusses issues with the NVIDIA Nemotron dataset, such as lack of system prompts, poor multi-turn handling, and absence of labeling
- 4The author suggests that datasets should include more metadata like instructions, constraints, tasks, skills, and user intents
Details
The author, who has been dabbling in AI since 2000, expresses concern about the lack of progress in creating high-quality datasets for AI, particularly for instruction-following tasks. They highlight a few datasets they consider the most well-rounded, such as Tulu, smoltalk2, and Hermes-3, but find that the overall quality and innovation in this area is lacking. The author considers WizzardLM and Magpie as the only real breakthroughs in dataset creation in recent times. They then discuss the issues they found with the NVIDIA Nemotron dataset, such as the lack of system prompts, poor handling of multi-turn conversations, and the absence of labeling and metadata that could help the language model learn better. The author suggests that datasets should include more detailed information, such as instructions, constraints, tasks, skills, and user intents, to improve the quality and performance of AI systems.
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