Researchers have developed an AI method called 'Adapting While Learning' that teaches small language models to decide when to use external tools versus internal knowledge. Using this approach, an 8 billion parameter model achieved significant improvements in accuracy and tool usage precision compared to larger models. This aligns with an industry trend towards smaller, more efficient AI models that perform well on specific tasks while using fewer resources.
Large Language Models (LLMs) have shown human-like capabilities in various tasks, sparking debate on whether they can truly reason or are just advanced pattern recognition systems. This challenges the equation of pattern recognition with genuine intelligence and reasoning.
