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Large Language Models (LLMs) such as GPT and Llama are driving exceptional innovations in AI, but research aimed at improving their explainability and reliability is constrained by massive resource requirements for examining and adjusting their behaviour.

To tackle this challenge, a Manchester research team led by Dr Danilo S. Carvalho and Dr Andr茅 Freitas have developed new software frameworks 鈥� LangVAE and LangSpace 鈥� that significantly reduces both hardware and energy resource needs for controlling and testing LLMs to build explainable AI.

Their technique builds compressed language representations from LLMs, making it possible to interpret and control these models using geometric methods (essentially treating the model鈥檚 internal language patterns as points and shapes in space that can be measured, compared and adjusted), without altering the models themselves. Crucially, their approach reduces computer resource usage by over 90% compared with previous techniques.

This leap in efficiency lowers the barriers to entry for developing explainable and controllable AI, opening the door for more researchers, startups and industry teams to explore how these powerful systems work.

Dr Carvalho explains, 鈥淲e have significantly lowered entry barriers for development and experimentation of explainable and controllable AI models and also hope to reduce the environmental impact of these research efforts.

鈥淥ur vision is to accelerate the development of trustable and reliable AI for mission-critical applications, such as healthcare.鈥�