Many domain experts do not have the time or expertise to write formal Bayesian models. This paper takes an informal problem description as input, and combines a large language model and a probabilistic programming language to define a joint distribution over formal models, latent variables, and data. A posterior over latent variables follows by conditioning on observed data and integrating over formal models. This presents a challenging inference problem. We suggest an inference recipe that amounts to generating many formal models from the large language model, performing approximate inference on each, and then doing a weighted average. This is justified and analyzed as a combination of self-normalized importance sampling, MCMC, and importance-weighted variational inference. Experimentally, this produces sensible predictions from only data and an informal problem description, without the need to specify a formal model.

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From Amazon's to Netflix's upcoming, the recent spate of hetero hockey romances shows Hollywood learned the wrong lessons from The streaming industry has gotten a lot of flak over the past few years, but there is one thing that Hollywood studios are undeniably good at: recycling the same idea, over and over and over again until the world ends (or until everyone finally decides they're sick of, whichever comes first). This tried-and-true formula is now playing out in real time with Prime Video's and Netflix's upcoming series Icebreaker shows that, like are hockey-themed romances about polar opposites who just can't seem to keep their hands off each other. But there's one key difference: and are about heterosexual romances, while is about a secret gay relationship. And considering how much queerness played a role in's explosive popularity, it seems like the clamor for straight horny hockey content is another example of Hollywood just not getting the message. The forthcoming which Netflix announced this week, is about a figure skater who falls in love with a hockey player after they're forced to practice on the same rink.

Neural Information Processing Systems http://nips.cc/

Deep learning (DL) systems have been gaining popularity in critical tasks such as credit evaluation and crime prediction.

Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)'s deficiency in lack of knowledge. Existing RAG datasets, however, do not adequately represent the diverse and dynamic nature of real-world Question Answering (QA) tasks. To bridge this gap, we introduce the Comprehensive RAG Benchmark (CRAG), a factual question answering benchmark of 4,409 question-answer pairs and mock APIs to simulate web and Knowledge Graph (KG) search. CRAG is designed to encapsulate a diverse array of questions across five domains and eight question categories, reflecting varied entity popularity from popular to long-tail, and temporal dynamisms ranging from years to seconds. Our evaluation on this benchmark highlights the gap to fully trustworthy QA.

We develop new models and algorithms for learning the temporal dynamics of thetopic polytopes andrelated geometric objects thatarise intopic model based inference.

Thissettingfrequently arisesinpractice because it is often straightforward to perform feature-engineering and produce a large number of potentially useful features in many machine learning problems. For example, in a typical equity forecasting model,n is around 3,000 (i.e., using 10 years of market data), whereas the number of potentially relevant features can be in the order of thousands [36, 24, 26, 12].