Hello everyone, welcome to the tech blog. Today I would like to share a very interesting article that introduces a method called INSTRUCTZERO that can automatically design suitable instructions for large language models. (Thisipick automatically designs appropriate instructions for large language models.) As we all know, the performance of large language models is greatly affected by the quality of input instructions. The traditional approach is to write instructions manually, but this approach has limitations.
HOW DOES INSTRUCTZERO SOLVE THIS PROBLEM?
First, you need to set up a training dataset for soft instructions and tasks, and then use an open-source large-scale language model to generate task-related instructions. Then, these instructions and input data are used to make predictions through the black-box large-scale language model to obtain prediction results. However, it is not enough to have predictions. You need to build a scoring function that scores the prediction against the original goal so that you can know how good or bad the prediction is. Next, the scoring function is combined with Bayesian optimization to update soft instructions, iteratively searching for the best instruction until the desired goal is reached. EXPERIMENTAL RESULTS SHOW THAT INSTRUCTZERO EXCELS ON MULTIPLE TASKS, ACHIEVING THE BEST RESULTS ON ALMOST ALL TASKS, AND ACHIEVING SIGNIFICANT PERFORMANCE GAINS ON SOME TASKS. This means that this approach can be leveraged to automate instruction generation for tasks, improving the performance and applicability of large language models. Overall, INSTRUCTZERO is an innovative way to automate the generation of task instructions to improve the performance of large language models on a variety of tasks. This is of great significance for the study of imperative learning and large models. If you are interested in this method, you can read the original article for more details.
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