Named entity recognition (NER) is usually the basis of transforming unstructured data (text) into structured data (say, in JSON format).
The current best approach for NER in ChatGPT is few-shot-learning, where we give a few examples and asks for it to solve ONE instance of the task.
The problem with this approach is the cost. We need to use a lot of tokens from examples in order to predict a single instance.
I'm trying to overcome this issue by giving more than one instance for prediction. Something like this:<p>```
<instruction>
<1 - example>
<2 - example>
<3 - new instance>
<4 - new instance><p><answer for 1>
<answer for 2>
```<p>Wishing the generator to predict answers for instances 3 and 4.<p>From my experience, results are worst than predicting a single instance.<p>Does anyone have better results in this task?<p>Thanks!