#Bobok summary code#

The Gambler is a breathtaking portrayal of an intense and futile obsession. Pacific: A spokesperson told VentureBeat that “OpenAI has no plans to make the book summarization model publicly available or open source.The stories in this volume demonstrate Dostoyevsky's genius for fusing caricature, irony and the grotesque to create a powerful dark humour. We’ve reached out to the company to see when - or if - it plans to make these public.

#Bobok summary code#

OpenAI hasn’t provided the source code or training dataset for the model. Going forward, we are researching better ways to assist humans in evaluating model behavior, with the goal of finding techniques that scale to aligning artificial general intelligence.” “Our progress on book summarization is the first large-scale empirical work on scaling alignment techniques. “This work is part of our ongoing research into aligning advanced AI systems, which is key to our mission,” OpenAI researchers Jeffrey Wu, Ryan Lowe, and Jan Leike wrote in a blog post. For example, it might be hard to catch cases where earlier details in the book are only later revealed to be important, as is true of mystery books. Task decomposition assumes that separate parts of a task can be completed independently, a rule that may not be true for summarizing books. Moreover, the model’s summaries often read more as a list of events from the book rather than a coherent summary, revealing the limitations of task decomposition. While the model successfully generated “book-level” summaries containing much of the important information, it also sometimes generated inaccurate statements due to a lack of context, OpenAI concedes in a paper.

To evaluate the model, the lab’s researchers took the 40 most popular books published in 2020 (according to Goodreads) and assigned two people to read each book and write a summary and then to rate summaries from both the model and each other. OpenAI trained the model on a subset of the books in GPT-3’s training dataset that were mostly of the fiction variety and contained over 100,000 words on average. Moreover, it enables the model to summarize books of any length, from tens of pages to hundreds or thousands. This decomposition allows humans to evaluate the model’s summaries quickly by using summaries of smaller parts of books. To create the model, OpenAI combined reinforcement learning with recursive task decomposition, which procedurally breaks up a difficult task (e.g., summarizing a long piece of text) into simpler, individual ones (e.g., summarizing several shorter pieces). Reinforcement learning entails training a system to perform a task - for example, summarizing text - by rewarding desired behaviors and/or punishing undesired ones. OpenAI’s new model builds on the company’s previous research, which found that training a model with reinforcement learning from human feedback helped align model summaries with people’s preferences on short posts and articles. MetaBeat will bring together thought leaders to give guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA.