Language
Can large language models figure out the real world?
Back in the 17th century, German astronomer Johannes Kepler figured out the laws of motion that made it possible to accurately predict where our solar system’s planets would appear in the sky as they orbit the sun. But it wasn’t until decades later, when Isaac Newton formulated the universal laws of gravitation, that the underlying…
Australia’s Large Language Model Landscape: Technical Assessment
Key Points No flagship, globally competitive, locally developed LLM (such as GPT-4, Claude 3.5, LLaMA 3.1) has yet emerged from Australia. Australian research and commerce currently rely primarily on international LLMs, which are frequently used but have measurable limitations on Australian English and cultural context. Kangaroo LLM is the only major open-source, locally developed LLM…
SEA-LION v4: Multimodal Language Modeling for Southeast Asia
AI Singapore (AISG) has released SEA-LION v4, an open-source multimodal language model developed in collaboration with Google and based on the Gemma 3 (27B) architecture. The model is designed to support Southeast Asian languages, including those with limited digital resources, and provides both text and image understanding capabilities. SEA-LION v4 uses a commercially permissive license…
Large Language Models LLMs vs. Small Language Models SLMs for Financial Institutions: A 2025 Practical Enterprise AI Guide
No single solution universally wins between Large Language Models (LLMs, ≥30B parameters, often via APIs) and Small Language Models (SLMs, ~1–15B, typically open-weights or proprietary specialist models). For banks, insurers, and asset managers in 2025, your selection should be governed by regulatory risk, data sensitivity, latency and cost requirements, and the complexity of the use…
Researchers glimpse the inner workings of protein language models
Within the past few years, models that can predict the structure or function of proteins have been widely used for a variety of biological applications, such as identifying drug targets and designing new therapeutic antibodies. These models, which are based on large language models (LLMs), can make very accurate predictions of a protein’s suitability for…
Meet SmallThinker: A Family of Efficient Large Language Models LLMs Natively Trained for Local Deployment
The generative AI landscape is dominated by massive language models, often designed for the vast capacities of cloud data centers. These models, while powerful, make it difficult or impossible for everyday users to deploy advanced AI privately and efficiently on local devices like laptops, smartphones, or embedded systems. Instead of compressing cloud-scale models for the…
Implementing Self-Refine Technique Using Large Language Models LLMs
This tutorial demonstrates how to implement the Self-Refine technique using Large Language Models (LLMs) with Mirascope, a powerful framework for building structured prompt workflows. Self-Refine is a prompt engineering strategy where the model evaluates its own output, generates feedback, and iteratively improves its response based on that feedback. This refinement loop can be repeated multiple…
The unique, mathematical shortcuts language models use to predict dynamic scenarios
Let’s say you’re reading a story, or playing a game of chess. You may not have noticed, but each step of the way, your mind kept track of how the situation (or “state of the world”) was changing. You can imagine this as a sort of sequence of events list, which we use to update…
You Don’t Need to Share Data to Train a Language Model Anymore—FlexOlmo Demonstrates How
The development of large-scale language models (LLMs) has historically required centralized access to extensive datasets, many of which are sensitive, copyrighted, or governed by usage restrictions. This constraint severely limits the participation of data-rich organizations operating in regulated or proprietary environments. FlexOlmo—introduced by researchers at the Allen Institute for AI and collaborators—proposes a modular training…
Mistral AI Releases Devstral 2507 for Code-Centric Language Modeling
Mistral AI, in collaboration with All Hands AI, has released updated versions of its developer-focused large language models under the Devstral 2507 label. The release includes two models—Devstral Small 1.1 and Devstral Medium 2507—designed to support agent-based code reasoning, program synthesis, and structured task execution across large software repositories. These models are optimized for performance…