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. Finally, get feedback on your article and make revisions to finalize it. Then, write a first draft that summarizes the article effectively. Before you start your summary, read the article several times and make notes in the margins. In this case, to evaluate book summaries we empower humans with individual chapter summaries written by our model, which saves them time when evaluating these summaries relative to reading the source text. An article summary provides an overview of the author’s thesis, purpose, and main ideas.
Our current approach to this problem is to empower humans to evaluate machine learning model outputs using assistance from other models. Therefore we want our ability to evaluate our models to increase as their capabilities increase. This makes it harder to detect subtle problems in model outputs that could lead to negative consequences when these models are deployed. This work is part of our ongoing research into aligning advanced AI systems, which is key to our mission. As we train our models to do increasingly complex tasks, making informed evaluations of the models’ outputs will become increasingly difficult for humans. Our method can be used to summarize books of unbounded length, unrestricted by the context length of the transformer models we use.See for yourself on our summary explorer! For example, you can trace to find where in the original text certain events from the summary happen. It is easier to trace the summary-writing process.Decomposition allows humans to evaluate model summaries more quickly by using summaries of smaller parts of the book rather than reading the source text.
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You don’t have to download it on your computer or pay a subscription or fee to use it. Save time When key points are clearly highlighted, you can stay informed without compromising your time.
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Compared to an end-to-end training procedure, recursive task decomposition has the following advantages: You can use our free summarizer to create a summary of an article. Summarize Focus your research Read through the clutter and zoom back into the sections where the details matter most. In this case we break up summarizing a long piece of text into summarizing several shorter pieces.
To address this problem, we additionally make use of recursive task decomposition: we procedurally break up a difficult task into easier ones. But judging summaries of entire books takes a lot of effort to do directly since a human would need to read the entire book, which takes many hours. In the past we found that training a model with reinforcement learning from human feedback helped align model summaries with human preferences on short posts and articles. Large pretrained models aren’t very good at summarization. Consider the task of summarizing a piece of text.
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