About Argilla
Build accurate language models with human and AI feedback. The feedback loops you need to fine-tune, validate, and maintain enterprise LLMs.
Argilla has helped us to adopt a data-centric approach in our processes to develop and maintain our NLP and ML solutions. We have gained a lot in terms of transparency and efficiency. In-house annotators, data scientists and engineers love it alike. After some time using it, we can't imagine our lives without Argilla!
Argilla and Prolific
Getting the best feedback for fine-tuning LLMs has never been easier. 🤖💡
Prolific now integrates with Argilla, an open-source tool that lets you collect AI data at scale - without sacrificing quality.
🔃 Plug Argilla into Prolific’s pool of 120k+ vetted and expert taskers.
🗣️ Gather the rich, human feedback you need to make LLMs robust and reliable.
🧠 AI automates the rest, quickly moving from prototype to ongoing maintenance.
Use cases
- LLM Monitoring and Evaluation
This process assesses LLM projects by collecting both human and machine feedback. Key to this is Argilla's integration with LangChain, which ensures continuous feedback collection for LLM applications.
- Collection of Demonstration Data
It facilitates the gathering of human-guided examples, necessary for supervised fine-tuning and instruction-tuning.
- Collection of Comparison Data
It plays a significant role in collecting comparison data to train reward models, a crucial component of LLM evaluation and RLHF
- Reinforcement Learning
It assists in crafting and selecting prompts for the reinforcement learning stage of RLHF
