Read between the lines -- Functionality Extraction From READMEs
원문 초록
While text summarization is a well-known NLP task, in this paper, we introduce a novel and useful variant of it called functionality extraction from Git README files. Though this task is a text2text generation at an abstract level, it involves its own peculiarities and challenges making existing text2text generation systems not very useful. The motivation behind this task stems from a recent surge in research and development activities around the use of large language models for code-related tasks, such as code refactoring, code summarization, etc. We also release a human-annotated dataset called FuncRead, and develop a battery of models for the task. Our exhaustive experimentation shows that small size fine-tuned models beat any baseline models that can be designed using popular black-box or white-box large language models (LLMs) such as ChatGPT and Bard. Our best fine-tuned 7 Billion CodeLlama model exhibit 70% and 20% gain on the F1 score against ChatGPT and Bard respectively.
함께 살펴볼 논문
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TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning
Leveraging Deep Learning for Abstractive Code Summarization of Unofficial Documentation
APE: Selective Fine-tuning with Acceptance Criteria for Language Model Adaptation
On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention
Improving large language models with concept-aware fine-tuning
Variational Prefix Tuning for Diverse and Accurate Code Summarization Using Pre-trained Language Models
토론
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