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Comment Generation

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.

Papers

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Showing 1–50 of 65 papers

TitleStatusHype
Leveraging Reward Models for Guiding Code Review Comment GenerationCode0
Enhancing Code Generation via Bidirectional Comment-Level Mutual GroundingCode0
ReviewAgents: Bridging the Gap Between Human and AI-Generated Paper Reviews—0
Mimicking the Familiar: Dynamic Command Generation for Information Theft Attacks in LLM Tool-Learning System—0
DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation—0
Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep LearningCode0
Prompting and Fine-tuning Large Language Models for Automated Code Review Comment Generation—0
DeepCodeProbe: Towards Understanding What Models Trained on Code Learn—0
Prompting for Numerical Sequences: A Case Study on Market Comment GenerationCode2
MESIA: Understanding and Leveraging Supplementary Nature of Method-level Comments for Automatic Comment GenerationCode0
OMPGPT: A Generative Pre-trained Transformer Model for OpenMP—0
Investigating the Efficacy of Large Language Models for Code Clone DetectionCode0
DiffuCom: A novel diffusion model for comment generation—0
LiveChat: Video Comment Generation from Audio-Visual Multimodal Contexts—0
A variational selection mechanism for article comment generationCode0
ViCo: Engaging Video Comment Generation with Human Preference Rewards—0
Steering Language Generation: Harnessing Contrastive Expert Guidance and Negative Prompting for Coherent and Diverse Synthetic Data Generation—0
Knowledge Enhanced Model for Live Video Comment GenerationCode0
APIContext2Com: Code Comment Generation by Incorporating Pre-Defined API Documentation—0
Error syntax aware augmentation of feedback comment generation dataset—0
Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help?—0
MOPRD: A multidisciplinary open peer review dataset—0
StoryER: Automatic Story Evaluation via Ranking, Rating and ReasoningCode0
Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator—0
Automatic Comment Generation via Multi-Pass DeliberationCode0
Automating Horizon Scanning in Future Studies—0
CoNT: Contrastive Neural Text GenerationCode2
InCoder: A Generative Model for Code Infilling and SynthesisCode2
LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition—0
Automating Code Review Activities by Large-Scale Pre-trainingCode1
Topic Analysis for Text with Side Data—0
Energy-bounded Learning for Robust Models of Code—0
Exploring Methods for Generating Feedback Comments for Writing LearningCode0
Impact of Evaluation Methodologies on Code SummarizationCode1
Shared Task on Feedback Comment Generation for Language Learners—0
Towards Controlled and Diverse Generation of Article Comments—0
Learning to Generate Code Comments from Class Hierarchies—0
API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations—0
Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection—0
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees—0
Learning to Represent Programs with Heterogeneous Graphs—0
Market Comment Generation from Data with Noisy Alignments—0
Learning with Contrastive Examples for Data-to-Text GenerationCode0
One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music CommentCode0
Towards Context-Aware Code Comment Generation—0
Retrieve and Refine: Exemplar-based Neural Comment GenerationCode1
MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection ModelsCode0
Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks—0
TAG : Type Auxiliary Guiding for Code Comment Generation—0
Creating Corpora for Research in Feedback Comment Generation—0
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