Category: AI

Many top language models now err on the side of caution, refusing harmless prompts that merely sound risky – an ‘over-refusal’ behavior that affects their usefulness in real-world scenarios. A new dataset called ‘FalseReject’ targets the…

The Future of Branding: AI in Logo Creation

In a world where first impressions often determine lasting perceptions, branding is everything. A company’s logo serves as the face of its identity, encapsulating values, aspirations, and culture in a…

A key question in artificial intelligence is how often models go beyond just regurgitating and remixing what they have learned and produce truly novel ideas or insights.

A new project from Google DeepMind shows that with a few clever tweaks these…

In this tutorial, we lean hard on Together AI’s growing ecosystem to show how quickly we can turn unstructured text into a question-answering service that cites its sources. We’ll scrape a handful of live web pages, slice them…

Identifying the exact location of a software issue—such as a bug or feature request—remains one of the most labor-intensive tasks in the development lifecycle. Despite advances in automated patch generation and code assistants,…

Reasoning language models, or RLMs, are increasingly used to simulate step-by-step problem-solving by generating long, structured reasoning chains. These models break down complex questions into simpler parts and build logical steps…

Imagine a radiologist examining a chest X-ray from a new patient. She notices the patient has swelling in the tissue but does not have an enlarged heart. Looking to speed up diagnosis, she might use a vision-language…

In the pretraining of LLMs, the quality of training data is crucial in determining model performance. A common strategy involves filtering out toxic content from the training corpus to minimize harmful outputs. While this approach…