Insights, tips, and product updates

Discover examples and techniques for developing domain-specific LLMs (Large Language Models) in this informative guide.

We've updated our article and are sharing 17 open-sourced datasets used for training LLMs, and the key steps to data preprocessing.

Reinforcement learning from human feedback (RLHF) allows large language models to understand human instruction naturally. This approach allows LLMs with lesser parameters to perform better. It also ensures the model avoids producing dangerous behavior. Still, RLHF is an evolving process, with challenges researchers must overcome.

We've updated our platform to provide data scientists and annotators with multilayer views for image annotations. Let's dive deep into how this approach can transform our image annotation process and improve model performance.

Explore OpenELM's specifics, focusing on its architecture, training methodologies, and the diverse datasets it has been trained on. Also, learn how to use it for domain-specific use cases.

Despite their compact size, which enables deployment on devices like smartphones, Microsoft's Phi-3 language models demonstrate impressive reasoning and language understanding capabilities that are competitive with and often exceed those of much larger open-source and proprietary models.

Let's deep dive into Llama 3. This article will provide a breakdown of what Meta has released about its training data so we can better understand how it was trained and what processes were used to build the high-performing LLM.

While standard LLMs excel in generalized tasks, real-world applications demand robust and domain-specific models. Thus, practitioners must fine-tune them on domain-specific data to suit downstream real-world tasks. This post explores fine-tuning LLMs, its benefits, challenges, approaches, and the steps involved.

If you're wondering whether your company should invest in a data labeling tool, keep reading. We've gathered the most common reasons why companies hesitate or decide to make the switch.

In our latest webinar recap: learn how insurance companies overcome training data challenges to ship their models faster.

Labeling videos is no easy task. To make it as efficient as possible for all our users, we’re happy to introduce an entirely new version of our video labeling tool! Read on to discover our new features.

A complete tutorial on video annotation using machine learning. Find how label video clips and get the best performance from your video annotation projects.

Today, we are pleased to unveil an advanced feature in our video annotation tool that supports long videos with thousands of objects in each frame.

AI document analysis helps businesses to process more documents at greater speeds, uncover patterns within data, combat fraud, and reduce errors.

Even with the advancements in AI, information extraction remains fraught with challenges. In this article, we will tackle the challenges of information extraction and the possible solutions to overcome them.

The use of artificial intelligence (AI) has become widespread. In today's world, artificial intelligence can be found in just about every aspect of technology, including vehicle insurance.

Master the best practices for data labeling, understand the impact of high quality data, and elevate your AI applications in the insurance industry.

Banks are amongst the most regulated establishments in the world. Discover how LCL built a powerful ID information extraction algorithm using Kili Technology to classify the IDs of their customers.

Named entity recognition & text classification are used to help companies understand and process natural language automatically. Read on to learn how.

Large Language Models for named entity recognition are a powerful tool that can save time and resources. Learn how to leverage the power of pre-trained language models with appropriate prompt design to perform NER on any named entity category without requiring task-specific training data.