High-Quality Data
By leveraging powerful models like GPT-4, along with tools such as search APIs and code interpreters, AgentInstruct ensures the generation of high-quality data.
Diverse Data
AgentInstruct produces both prompts and responses using a large number of agents equipped with powerful LLMs, various tools, and reflection flows.
It employs a taxonomy with over 100 subcategories to ensure diversity and quality in the prompts and responses generated.
Large Quantities of Data
Operating autonomously, AgentInstruct can generate vast amounts of data, applying flows for verification and filtering. It eliminates the need for seed prompts by using raw documents for seeding.
This challenge stems from the difficulty in creating high-quality and diverse synthetic data, which necessitates significant human effort in curation and filtering.
A common approach involves using powerful models like GPT-4 to generate responses to a set of prompts. This process is often enhanced by eliciting explanations or step-by-step instructions and employing complex prompting techniques to improve answer quality.
Using raw data (such as unstructured text documents or source code) as seeds offers two advantages. Firstly, it is abundant, enabling the creation of vast and diverse datasets using AgentInstruct.
Secondly, bypassing existing prompts, either as-is or after paraphrasing, can foster learning of more general capabilities rather than specific benchmarks.
The AgentInstruct approach is conducive to enhancing larger, more capable models due to its ability to generate new prompts and produce responses of higher quality than the LLM used in the agentic flow, facilitated by tools and reflection.
The AgentInstruct approach offers an effective solution for generating diverse, high-quality data for model post-training.
This method employs agentic flows to create synthetic data, addressing common issues such as lack of diversity and the need for extensive human curation.
By leveraging an agentic framework, AgentInstruct can produce custom datasets from unstructured data sources, enhancing model training and skill development.
The approach’s effectiveness is evidenced by the improved performance of the Orca-3 model, which benefited from a 25 million pair dataset generated by AgentInstruct.
The researchers believe that using agentic flows for synthetic data creation is valuable for all stages of model training, including pre-training, post-training, and domain/task specialisation.
This capability to generate diverse, high-quality instruction data from unstructured content could lead to partial or completely automated pipelines for model customisation and continuous improvement.
✨ Follow me on LinkedIn for updates on Large Language Models
I’m currently the Chief Evangelist @ Kore AI. I explore & write about all things at the intersection of AI & language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces & more.
