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View Code? Open in Web Editor NEWData and code for the ICLR 2023 paper "Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning".
License: MIT License
Data and code for the ICLR 2023 paper "Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning".
License: MIT License
Hi,
I am working on a method for exemplar selection for in-context learning and would like to compare it with PromptPG. From my understanding of the method, the set of candidate examples is kept the same between training and inference, but this doesn't seem to be the case in the implementation (comparing learn_policy.py:32 and run_gpt3.py:32). Is this supposed to be the case?
Thanks,
Shivanshu
Hello, I've discovered that RL chose similar exemplars even when the query questions are different based on what I've learned from both the training logs and the resulting json files. Put otherwise, a certain number of exemplars were chosen at high frequencies.
Does this suggest that those exemplars are in any way representative of the task or dataset?
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