2024 · 72 citations · 30 references
ComputingComputer ScienceComputational LinguisticsNatural Language ProcessingProgramming LanguagesLanguage ModelingLanguage ModelsNatural Language GenerationNatural Language UnderstandingText GenerationText SummarizationAutomated Text GenerationCollecting SemanticsData Flow PatternsLanguage Model PromptingNatural Language Explanation GenerationPromptingSummarization
Large Language Models (LLM) are a new class of computation engines, "programmed" via prompt engineering. Researchers are still learning how to best "program" these LLMs to help developers. We start with the intuition that developers tend to consciously and unconsciously collect semantics facts, from the code, while working. Mostly these are shallow, simple facts arising from a quick read. For a function, such facts might include parameter and local variable names, return expressions, simple pre- and post-conditions, and basic control and data flow, etc.
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Zhangyin Feng, Daya Guo, Duyu Tang et al. · 2020 · 2.4K citations · Full text
Yue Wang, Weishi Wang, Shafiq Joty et al. · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021 · 1.3K citations · Full text
Summarizing Source Code using a Neural Attention Model
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung et al. · 2016 · 725 citations · Full text