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Researchers have developed a set of tools that look inside large language models to detect when they go wrong, including hallucinations, memorization of training data and other forms of unreliable output.
Rather than relying only on a model’s final answer, the approach analyzes internal signals produced during computation, including activation patterns, attention maps and output probability distributions. The goal is to identify signs of failure as the model generates text.
These challenges are a major focus of the research group led by Dr. Haggai Maron from the Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering at the Technion – Israel Institute of Technology, in collaboration with researchers from other universities and NVIDIA.