![]() ![]() More recently, chain-of-thought (CoT) prompting (opens in a new tab) has been popularized to address more complex arithmetic, commonsense, and symbolic reasoning tasks. In other words, it might help if we break the problem down into steps and demonstrate that to the model. If you take a closer look, the type of task we have introduced involves a few more reasoning steps. The example above provides basic information on the task. It seems like few-shot prompting is not enough to get reliable responses for this type of reasoning problem. Let's first try an example with random labels (meaning the labels Negative and Positive are randomly assigned to the inputs): additional results show that selecting random labels from a true distribution of labels (instead of a uniform distribution) also helps.the format you use also plays a key role in performance, even if you just use random labels, this is much better than no labels at all."the label space and the distribution of the input text specified by the demonstrations are both important (regardless of whether the labels are correct for individual inputs)".(2022) (opens in a new tab), here are a few more tips about demonstrations/exemplars when doing few-shot: For more difficult tasks, we can experiment with increasing the demonstrations (e.g., 3-shot, 5-shot, 10-shot, etc.).įollowing the findings from Min et al. We can observe that the model has somehow learned how to perform the task by providing it with just one example (i.e., 1-shot). ![]()
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