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While large language models (LLMs) equipped with techniques like chain-of-thought prompting have demonstrated impressive capabilities, they still fall short in their ability to reason robustly in complex settings. However, evaluating LLM reasoning is challenging because system capabilities continue to grow while benchmark datasets for tasks like logical deduction have remained static. We introduce MuSR, a dataset for evaluating language models on multistep soft reasoning tasks specified in a natural language narrative. This dataset has two crucial features. First, it is created through a novel neurosymbolic synthetic-to-natural generation algorithm, enabling the construction of complex reasoning instances that challenge GPT-4 (e.g., murder mysteries roughly 1000 words in length) and which can be scaled further as more capable LLMs are released. Second, our dataset instances are free text narratives corresponding to real-world domains of reasoning; this makes it simultaneously much more challenging than other synthetically-crafted benchmarks while remaining realistic and tractable for human annotators to solve with high accuracy. We evaluate a range of LLMs and prompting techniques on this dataset and characterize the gaps that remain for techniques like chain-of-thought to perform robust reasoning.

I decided to take ChatGPT3 to a writing class. I mean a real writing class. One that takes you seriously and tries to get a story out of your soul. One that works hard to help you when you fail to express yourself and helps you find your way. ChatGPT3 is an impressive piece of technology.

It's simple math, really: In a family with eight children, it stands to reason, surely one of them must be queer. Bridgerton has defied other expectations of a Regency-era love story: It is set in an alternate universe where the upper class is fully integrated and race is not an issue. The show's first two seasons focus on interracial romances, and the second season at least obliquely references the history of British colonialism in India. There's one obvious candidate for such a storyline: On the show, Eloise is the most outspoken, most feminist Bridgerton sibling. She is not interested in becoming a debutante, delaying her appearance to pursue another year of studies. She often dismisses marriage, questioning why a husband and children are all that are waiting in store for women.

Artificial intelligence (AI) could diagnose a suspected dementia patient the day they are assessed. The disease currently has no set test, with medics generally relying on cognitive assessments and brain scans. With it sometimes taking years to reach a diagnosis, scientists from the University of Cambridge are developing an AI system that could spot signs of the disease after a single brain scan. The system is "trained" to compare a suspected patient's brain scan against thousands of confirmed cases, with the algorithm potentially identifying signs of the disease that a neurologist could not spot. Although the technology is still in a trial stage, it could diagnose dementia years before symptoms emerge.

AI can spot patterns in brain scans that even the most experienced neurologists can't see Scientists are testing an artificial intelligence system they believe could lead to a diagnosis of dementia following a brain scan. It can also predict whether the condition will remain stable for many years, worsen gradually, or whether the patient will require immediate treatment. Currently, a number of tests and CT scans are needed to diagnose dementia. Researchers involved in the study say that early diagnosis with the system they have developed can significantly improve patients' prognosis. "If we intervene earlier, treatments can act earlier and delay the progression of the disease, and at the same time, if we intervene earlier, can prevent further damage." of the Alan Turing Institute of Artificial Intelligence and Data Science.