LLM Limitations Explored
Tim and Nicholas delve into the unexpected failure modes of LLMs, highlighting their struggles with tasks requiring accuracy and correctness. Nicholas shares a surprising experience where a complex algorithm was solved efficiently by a model, contrasting it with trivial tasks that often lead to errors. The conversation raises concerns about users' ability to discern correct answers, emphasizing the need for critical engagement with AI outputs.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Nicholas Carlini (Google DeepMind)
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