Papers by Wen-Ding Li
The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations? (2024.findings-acl)
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| Challenge: | Language models are more proficient at code generation, but they still generate incorrect programs. |
| Approach: | They define a group of models that have a high log-probability and weak correctness checks. |
| Outcome: | The proposed model samples fail to understand counterfeits through three clear failure modes . counterfeits are confusing to the model as they are to other models, the authors say . |
Natural Language to Code Generation in Interactive Data Science Notebooks (2023.acl-long)
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Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Oleksandr Polozov, Charles Sutton
| Challenge: | Data scientists use computational notebooks to perform data wrangling and analytic tasks. |
| Approach: | They build a benchmark program that synthesizes programs given NL intents from users by using a Python code language model. |
| Outcome: | The proposed model outperforms public code LMs in a dataset of 1078 code generation problems using the pandas data analysis framework in data science notebooks. |