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Methodology
← Generation
Natural Language Processing
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Generation
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Text Generation
4534 directly classified papers
Papers per year
2011: 1
2015: 2
2016: 20
2017: 75
2018: 165
2019: 353
2020: 383
2021: 510
2022: 612
2023: 620
2024: 804
2025: 868
2026: 121
Papers
Grammar-Aligned Decoding
NIPS 2024
Selective Generation for Controllable Language Models
NIPS 2024
De-Diffusion Makes Text a Strong Cross-Modal Interface
CVPR 2024
Order-Independence Without Fine Tuning
NIPS 2024
Inevitable Trade-off between Watermark Strength and Speculative Sampling Efficiency for Language Models
NIPS 2024
SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
NIPS 2024
Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation
NIPS 2024
EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization
NIPS 2024
INDICT: Code Generation with Internal Dialogues of Critiques for Both Security and Helpfulness
NIPS 2024
How Does the Disclosure of AI Assistance Affect the Perceptions of Writing?
EMNLP 2024
LUQ: Long-text Uncertainty Quantification for LLMs
EMNLP 2024
Towards Automatic Composition of ASP Programs from Natural Language Specifications
IJCAI 2024
Beyond What If: Advancing Counterfactual Text Generation with Structural Causal Modeling
IJCAI 2024
XAI-Lyricist: Improving the Singability of AI-Generated Lyrics with Prosody Explanations
IJCAI 2024
RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework
EMNLP 2024
Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model Prompting
EMNLP 2024
Satyrn: A Platform for Analytics Augmented Generation
EMNLP 2024
Detection and Measurement of Syntactic Templates in Generated Text
EMNLP 2024
Language-to-Code Translation with a Single Labeled Example
EMNLP 2024
Towards Verifiable Text Generation with Evolving Memory and Self-Reflection
EMNLP 2024
Explaining and Improving Contrastive Decoding by Extrapolating the Probabilities of a Huge and Hypothetical LM
EMNLP 2024
Assessing “Implicit” Retrieval Robustness of Large Language Models
EMNLP 2024
TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts
EMNLP 2024
“A good pun is its own reword”: Can Large Language Models Understand Puns?
EMNLP 2024
MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language
EMNLP 2024
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