Datasaur Introduces LLM Tool to Train Custom ChatGPT Models

Datasaur, a data labeling platform, has introduced a new tool that enables users to label data and train their own customized ChatGPT model. The latest tool is an easy-to-use interface that allows both technical and non-technical individuals to score language model responses, transforming them into actionable insights. The company aims to provide comprehensive support for users in assembling their training data, helping to remove unwanted biases from the resulting model through their new offerings. Professionals across various industries, especially those eager to harness AI more effectively, have had difficulties in fine-tuning and improving the performance of the numerous open-source natural language processing models available. With Evaluation, human annotators can evaluate the quality of the models’ outputs and determine whether they meet specific quality criteria. Reinforcement Learning from Human Feedback (RLHF) facilitates the process of learning from this human feedback. According to the company, it has the potential to reduce the time and expenses associated with data labeling by 30% to 80%. The platform uses a range of techniques to automate data labeling, such as incorporating a built-in OpenAI API. The platform also facilitates the identification and resolution of discrepancies among annotators.