Key considerations include:
- Ensuring readability and interpretability of LLM-generated information to facilitate human understanding.
- Implementing upstream knowledge enrichment or filtering to optimise human resource use and reduce time spent on low-value tasks.
- Adding engaging interactive features to make data processing tasks more enjoyable and attract a wider audience.
In traditional crowdsourced annotation, workers receive a codebook detailing the task purpose, data explanation, and background knowledge to better understand their jobs.
Similarly, for LLM-driven data generation, task specification is crucial and can include role-play, format clarification, and knowledge augmentation.
A simple prompt like suppose you are a {xxx} can significantly improve LLM performance by setting the right context . This approach reminds of another study, where the researchers propose a new persona-driven data synthesis method that uses different perspectives within a large language model (LLM) to create varied synthetic data.
To support this method on a large scale, they introduce Persona Hub, a collection of 1 billion diverse personas automatically gathered from web data.
To ensure valid supervision, generated data must be logically and grammatically coherent.
However, inherent issues like hallucination and the fat-tailed knowledge distribution in large language models (LLMs) can introduce significant noise. This often leads to factual errors, incorrect labels, or irrelevant content, particularly when generating long, complex, or domain-specific data.
Diversity refers to the variations in generated data, such as differences in text length, topic, and writing style.
It is crucial for creating synthetic samples that reflect the diversity of real-world data, which helps prevent overfitting and bias during model training or evaluation.
However, inherent biases in large language models (LLMs) often result in monotonous and less diverse content, limiting its usefulness in downstream tasks.
The aim of synthetic data is not to imbue the target model knowledge, but rather train the model on certain personas and special abilities like advanced reasoning or task decomposition.
By combining strong data discovery and data design practices within a well-structured data topology, the process of creating synthetic data becomes more efficient, accurate, and aligned with real-world needs.
This foundational layer is essential for generating high-quality synthetic data that can effectively train and validate machine learning models.
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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.
