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Found 4 papers, 1 papers with code
The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations?
In this work, we focus on these counterfeit samples: programs sampled from a language model that 1) have a high enough log-probability to be generated at a moderate temperature and 2) pass weak correctness checks.
Guess & Sketch: Language Model Guided Transpilation
In this work, we leverage the strengths of LMs and symbolic solvers in a neurosymbolic approach to learned transpilation for assembly code.
Mixture of Soft Prompts for Controllable Data Generation
Large language models (LLMs) effectively generate fluent text when the target output follows natural language patterns.
Toward Code Generation: A Survey and Lessons from Semantic Parsing
With the growth of natural language processing techniques and demand for improved software engineering efficiency, there is an emerging interest in translating intention from human languages to programming languages.
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