The story starts here: does your model generate accurate responses to a given prompt? What makes a great response that reads naturally and also provides a clear reply to the prompt?
Related Processes
The story starts here: does your model generate accurate responses to a given prompt? What makes a great response that reads naturally and also provides a clear reply to the prompt?
Apply grades and metadata to train the model on the basic requirements of a response. For example, does a given response represent a hallucination? Does another response represent an incomplete reply to a given prompt? Configure the metadata you would like to apply and our team will do the rest.
Copy edit responses to train models on how to take a more natural tone. Combine responses to bring two incomplete replies together to create an exemplary response to a prompt.
Apply grades and metadata to train the model on the basic requirements of a response. For example, does a given response represent a hallucination? Does another response represent an incomplete reply to a given prompt? Configure the metadata you would like to apply and our team will do the rest.
Related Processes
Copy edit responses to train models on how to take a more natural tone. Combine responses to bring two incomplete replies together to create an exemplary response to a prompt.
Related Processes
Reinforcement Learning with Human Feedback (RLHF) is a subfield of Reinforcement Learning (RL) that involves incorporating feedback from human evaluators and a reward system to improve the learning process. It’s like giving a dog a treat for doing a new trick. The goal of RLHF is to improve the efficiency and effectiveness of RL algorithms by using human feedback to guide the learning process.
The problem: It’s really hard to scale.
To get the most out of RLHF trained models, you need a lot of skilled data trainers to prepare data and give the model intelligent & consistent feedback. Invisible offers one of the only cost-effective solutions in the market.
Learn more about RLHF from the experts who pioneered it.
Reinforcement Learning Form Human Feedback (RLHF) is a subfield of Reinforcement Learning (RL) that involves incorporating feedback from human evaluators and a reward system to improve the learning process.
The problem: It’s really hard to scale.
To get the most out of RLHF trained models, you need a lot of skilled data trainers to prepare data and give the model intelligent & consistent feedback. Invisible offers one of the only cost-effective solutions in the market.
Business leaders are overcoming obstacles created by hiring freezes by implementing AI technology technology. Most say they’re deploying AI to make smarter products.
Invisible CTO Scott Downes joined DataFramed recently to discuss how ChatGPT and Generative AI are augmenting workflows and scale operations.
Invisible has done outstanding work that has materially increased the team’s productivity...we plan to expand our services with invisible.
Invisible is our strategic growth partner providing us with business intelligence to expand into new markets. They exceeded our expectations in both cost and quality while improving our outcomes.
