This academic paper discusses the concept of introspective planning for Large Language Models (LLMs). These models, which are used in robots to understand natural language instructions and plan actions, often…
Continue readingThe paper introduces a new method for text generation termed as non-exchangeable conformal nucleus sampling. This method is a novel extension of the conformal prediction framework, which is used for…
Continue readingThe paper investigates the utility of conformal prediction sets in AI-advised decision-making. Through an experiment, it compares conformal prediction sets to displays of Top-1 and Top-k predictions for AI-advised image…
Continue readingThe research paper presents a novel uncertainty modeling framework for self-explaining neural networks. The framework aims to enhance the interpretability of deep neural networks (DNNs), which are often seen as…
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