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Comment Generation
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Article commenting poses new challenges for machines, as it involves multiple cognitive abilities: understanding the given article, formulating opinions and arguments, and organizing natu ral language for expression.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Enhancing Code Generation via Bidirectional Comment-Level Mutual Grounding
ReviewAgents: Bridging the Gap Between Human and AI-Generated Paper Reviews
Mimicking the Familiar: Dynamic Command Generation for Information Theft Attacks in LLM Tool-Learning System
DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation
Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning
Prompting and Fine-tuning Large Language Models for Automated Code Review Comment Generation
DeepCodeProbe: Towards Understanding What Models Trained on Code Learn
Prompting for Numerical Sequences: A Case Study on Market Comment Generation
MESIA: Understanding and Leveraging Supplementary Nature of Method-level Comments for Automatic Comment Generation
OMPGPT: A Generative Pre-trained Transformer Model for OpenMP
Investigating the Efficacy of Large Language Models for Code Clone Detection
LiveChat: Video Comment Generation from Audio-Visual Multimodal Contexts
ViCo: Engaging Video Comment Generation with Human Preference Rewards
Steering Language Generation: Harnessing Contrastive Expert Guidance and Negative Prompting for Coherent and Diverse Synthetic Data Generation
Knowledge Enhanced Model for Live Video Comment Generation
APIContext2Com: Code Comment Generation by Incorporating Pre-Defined API Documentation
Error syntax aware augmentation of feedback comment generation dataset
Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help?
MOPRD: A multidisciplinary open peer review dataset
StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning
Automatic Comment Generation via Multi-Pass Deliberation
CoNT: Contrastive Neural Text Generation
InCoder: A Generative Model for Code Infilling and Synthesis
LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition
Automating Code Review Activities by Large-Scale Pre-training
Topic Analysis for Text with Side Data
Energy-bounded Learning for Robust Models of Code
Impact of Evaluation Methodologies on Code Summarization
Towards Controlled and Diverse Generation of Article Comments
Learning to Generate Code Comments from Class Hierarchies
API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations
Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees
Learning to Represent Programs with Heterogeneous Graphs
Retrieve and Refine: Exemplar-based Neural Comment Generation
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