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What is Pareto?
Pareto is a data collection platform designed to enhance AI and LLM model training by providing access to a network of highly skilled and vetted data labelers. The platform prioritizes a talent-first approach, ensuring that AI companies receive the highest quality data for fine-tuning their models.
Pareto's globally distributed network includes master annotators, evaluators, and prompt engineers with extensive experience. These professionals have collectively completed over 3,000,000 tasks for major AI/ML organizations, ensuring a high level of expertise. Pareto offers cost-effective, on-demand pricing and supports ethical crowd work through equitable compensation and collaborative management.
Features
- RLHF: Human feedback on a model's output through demonstrations, evaluations, or ranking of its generated results.
- Side-by-Side Model Comparison: Evaluation and ranking of outputs across multiple models.
- Unique Original Content: Creation and collection of unique prompts and demonstrations of ideal responses.
- Creative Hallucination Inducement: Applying strategies to induce models to break rules and determine areas that need improvement.
- Honesty and Factuality Training: Evaluation and feedback to ensure model responses are honest and factual.
- AI Safety, Compliance, and Ethics Training: Identifying and addressing problematic, harmful, and illegal model behavior.
- In-Depth Engine Annotation: Annotating every step of the LLM process.
- Data Classification & Indexing: Classification and comparison of LLM data, including text, images, audio, questions, and prompts.
- Prompt Creation: Crafting highly effective prompts to stimulate creative responses from LLMs.
- Term Relevancy and Item Fluency Grading: Ensuring LLM outputs are contextually relevant and fluently constructed.
Use Cases
- Fine-tuning AI/LLM models
- Collecting high-quality and challenging datasets
- Running non-conventional or experimental AI projects
- Scaling up for fast task completion
- Improving model accuracy, relevance, and quality
- Training AI for creativity and originality
- Ensuring AI safety, compliance, and ethical behavior
- Data labeling and dataset creation for model training
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