Uncover the latest AI trends in Appen's 2024 State of AI Report.

High-quality data for large language model builders

Leading provider of custom, high-quality data for Large Language Model builders used for training and evaluation. Experience delivering hundreds of projects with top foundation model builders with diverse requirements across different use cases, languages, and domain expertise

Fine tuning & instruction datasets

Leverage the collective expertise of Appen’s qualified domain experts to create custom prompts and responses to improve your model’s performance. Our projects often involve different use cases (e.g. Q&A, summarization, Chain-of-Thought reasoning), specialized domains (e.g. math, finance, coding), and multiple languages (e.g. English, Spanish, Japanese). Our internal LLM data experts will help ensure consistent, high-quality datasets with our language expertise, mature quality assurance processes, and experienced crowd management.

Human Preference Ranking

Incorporate human feedback and alignment into LLM training using Appen’s AI Chat Feedback tool which enables real-time interactions with your LLM. Conduct single or multi-turn conversations and design custom workflows for your training requirements, including high-quality data creation for Reinforcement Learning with Human Feedback (RLHF) or Direct Preference Optimization (DPO).

LLM evaluation & benchmarking

Understand the performance of LLMs across a range of evaluation metrics such as relevance, accuracy, helpfulness, and coherence. Through evaluation against target criteria, we provide insights into the relative strengths and areas for improvement to guide targeted improvements in model training.

LLM evaluation can be stand-alone for a specific model, involve A/B testing of different model versions, or provide benchmarking against other LLMs in the market.

Red teaming

Ensure the safety and security of Large Language Models (LLMs) by proactively identifying risks before they arise. Leverage Appen’s trusted red teaming crowd to conduct a structured simulation of adversarial attacks. Our red teaming report provides analysis and insights to help you understand your model’s performance in response to different attack types and harm categories like toxicity, bias, misinformation, and privacy.

Trusted by 80% of the world's top model builders.

High quality data at scale

Discover how Appen accelerates the development of your AI applications.

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