Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Yanggang Wang, Haiyu Li, Zhilin Yang
We lifted 6 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| hwxu20/gps | — | 2 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| augment_prompt | Ran | hwxu20/gps/ga_processer_t0.py code served (permissive licence) · get_code("cbf6db8c3c705cb2") |
| read_task_template | Ran | hwxu20/gps/ga_processer_t0.py code served (permissive licence) · get_code("e3b4b752ebd7fb73") |
| _dump_template | Not yet run | hwxu20/gps/ga_processer_t0.py code served (permissive licence) · get_code("db61c525851ad1e1") |
| check_configs | Not yet run | hwxu20/gps/ga_processer_t0.py code served (permissive licence) · get_code("f6de4aa5f12368f0") |
| ga_process | Not yet run | hwxu20/gps/ga_processer_t0.py code served (permissive licence) · get_code("ad8edaebcd28713e") |
| run_test | Not yet run | hwxu20/gps/ga_processer_t0.py code served (permissive licence) · get_code("9ef8c9c9ac454d25") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Prompt-based techniques have demostrated great potential for improving the few-shot generalization of pretrained language models. However, their performance heavily relies on the manual design of prompts and thus requires a lot of human efforts. In this paper, we introduce Genetic Prompt Search (GPS) to improve few-shot learning with prompts, which utilizes a genetic algorithm to automatically search for high-performing prompts. GPS is gradient-free and requires no update of model parameters but only a small validation set. Experiments on diverse datasets proved the effectiveness of GPS, which outperforms manual prompts by a large margin of 2.6 points. Our method is also better than other parameter-efficient tuning methods such as prompt tuning.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2210.17041")
get_code_for_paper("2210.17041")
have("2210.17041")
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