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# Who cites «The WebNLG Challenge: Generating Text from RDF Data»

237 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/gardent-2017-webnlg-challenge-generating-text-rdf/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/gardent-2017-webnlg-challenge-generating-text-rdf-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/gardent-2017-webnlg-challenge-generating-text-rdf-3/)

1. [LLM Multi-Agent Systems for Long Triple Set Data-to-Text Generation](https://aclanthology.org/2026.findings-acl.1712/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
2. [The Shared Task on Reproducibility of Evaluations in NLP (ReproNLP) 2026: Overview and Results](https://aclanthology.org/2026.gem-main.83/) *— 2026 · Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)*
3. [Thesis Proposal: Intentional Inference for Insight Generation](https://aclanthology.org/2026.acl-srw.109/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
4. [Evaluating and Improving Graph to Text Generation with Large Language Models](https://aclanthology.org/2025.naacl-long.513/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
5. [Improving Factuality with Explicit Working Memory](https://aclanthology.org/2025.acl-long.548/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
6. [On Reducing Factual Hallucinations in Graph-to-Text Generation Using Large Language Models](https://aclanthology.org/2025.genaik-1.5/) *— 2025 · Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK)*
7. [PARAPHRASUS: A Comprehensive Benchmark for Evaluating Paraphrase Detection Models](https://aclanthology.org/2025.coling-main.585/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
8. [Standard Quality Criteria Derived from Current NLP Evaluations for Guiding Evaluation Design and Grounding Comparability and AI Compliance Assessments](https://aclanthology.org/2025.findings-acl.1370/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
9. [StructFact: Reasoning Factual Knowledge from Structured Data with Large Language Models](https://aclanthology.org/2025.findings-acl.391/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
10. [WebNLG-IT: Construction of an aligned RDF-Italian corpus through Machine Translation techniques](https://aclanthology.org/2025.findings-acl.625/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
11. [AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning](https://aclanthology.org/2024.naacl-long.282/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
12. [Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation](https://aclanthology.org/2024.acl-long.651/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [Bi-Directional Multi-Granularity Generation Framework for Knowledge Graph-to-Text with Large Language Model](https://aclanthology.org/2024.acl-short.14/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
14. [DCU-ADAPT-modPB at the GEM''24 Data-to-Text Generation Task: Model Hybridisation for Pipeline Data-to-Text Natural Language Generation](https://aclanthology.org/2024.inlg-genchal.7/) *— 2024 · Proceedings of the 17th International Natural Language Generation Conference: Generation Challenges*
15. [Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics](https://aclanthology.org/2024.lrec-main.423/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
16. [Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering](https://aclanthology.org/2024.emnlp-main.594/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
17. [Exploring Data Augmentation in Neural DRS-to-Text Generation](https://aclanthology.org/2024.eacl-long.132/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
18. [Exploring the Synergy of Dual-path Encoder and Alignment Module for Better Graph-to-Text Generation](https://aclanthology.org/2024.lrec-main.612/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
19. [FIRE: A Dataset for Financial Relation Extraction](https://aclanthology.org/2024.findings-naacl.230/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
20. [Fine-grained Hallucination Detection and Editing for Language Models](https://arxiv.org/abs/2401.06855) *— 2024 · arXiv:2401.06855*
21. [Graph Language Models](https://aclanthology.org/2024.acl-long.245/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
22. [Large Language Models for Scientific Information Extraction: An Empirical Study for Virology](https://aclanthology.org/2024.findings-eacl.26/) *— 2024 · Findings of the Association for Computational Linguistics: EACL 2024*
23. [LoRA Meets Dropout under a Unified Framework](https://aclanthology.org/2024.findings-acl.119/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
24. [PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs](https://aclanthology.org/2024.findings-acl.400/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
25. [Probing Omissions and Distortions in Transformer-based RDF-to-Text Models](https://arxiv.org/abs/2409.16707) *— 2024 · arXiv:2409.16707*
26. [Prompting for Numerical Sequences: A Case Study on Market Comment Generation](https://aclanthology.org/2024.lrec-main.1155/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
27. [SPOR: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text Generation](https://aclanthology.org/2024.acl-long.36/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation](https://aclanthology.org/2024.findings-naacl.106/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
29. [Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks](https://aclanthology.org/2024.findings-emnlp.688/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
30. [A Sequence-to-Sequence&Set Model for Text-to-Table Generation](https://aclanthology.org/2023.findings-acl.330/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
31. [Better Translation + Split and Generate for Multilingual RDF-to-Text (WebNLG 2023)](https://aclanthology.org/2023.mmnlg-1.8/) *— 2023 · Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)*
32. [CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality](https://aclanthology.org/2023.acl-long.168/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
33. [DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models](https://aclanthology.org/2023.acl-long.456/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
34. [Discourse-Aware Text Simplification: From Complex Sentences to Linked Propositions](https://arxiv.org/abs/2308.00425) *— 2023 · arXiv:2308.00425*
35. [DyLoRA: Parameter-Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation](https://aclanthology.org/2023.eacl-main.239/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
36. [Evaluating Generative Models for Graph-to-Text Generation](https://aclanthology.org/2023.ranlp-1.133/) *— 2023 · Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing*
37. [FactKG: Fact Verification via Reasoning on Knowledge Graphs](https://aclanthology.org/2023.acl-long.895/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
38. [Faithful Low-Resource Data-to-Text Generation through Cycle Training](https://aclanthology.org/2023.acl-long.160/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
39. [GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator](https://aclanthology.org/2023.acl-long.522/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
40. [Generating and Answering Simple and Complex Questions from Text and from Knowledge Graphs](https://aclanthology.org/2023.ijcnlp-main.19/) *— 2023 · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [Hallucination Mitigation in Natural Language Generation from Large-Scale Open-Domain Knowledge Graphs](https://aclanthology.org/2023.emnlp-main.770/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
42. [INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback](https://aclanthology.org/2023.emnlp-main.365/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
43. [Is GPT-4 a Good Data Analyst?](https://aclanthology.org/2023.findings-emnlp.637/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
44. [MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation](https://aclanthology.org/2023.findings-acl.704/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
45. [Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models](https://aclanthology.org/2023.eacl-main.176/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
46. [Reranking for Natural Language Generation from Logical Forms: A Study based on Large Language Models](https://aclanthology.org/2023.ijcnlp-main.69/) *— 2023 · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
47. [SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes](https://aclanthology.org/2023.acl-long.283/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
48. [Structure-aware Knowledge Graph-to-text Generation with Planning Selection and Similarity Distinction](https://aclanthology.org/2023.emnlp-main.537/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
49. [TaTA: A Multilingual Table-to-Text Dataset for African Languages](https://aclanthology.org/2023.findings-emnlp.118/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
50. [The 2023 WebNLG Shared Task on Low Resource Languages. Overview and Evaluation Results (WebNLG 2023)](https://aclanthology.org/2023.mmnlg-1.6/) *— 2023 · Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)*
51. [The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding](https://aclanthology.org/2023.findings-acl.351/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
52. [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://arxiv.org/abs/2306.08302) *— 2023 · arXiv:2306.08302 · IEEE Transactions on Knowledge and Data Engineering (TKDE) 2024*
53. [Unsupervised Graph-Text Mutual Conversion with a Unified Pretrained Language Model](https://aclanthology.org/2023.acl-long.281/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
54. [Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs](https://aclanthology.org/2023.mmnlg-1.5/) *— 2023 · Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)*
55. [AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning](https://aclanthology.org/2022.emnlp-main.388/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
56. [AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models](https://aclanthology.org/2022.findings-emnlp.240/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
57. [Block Diagram-to-Text: Understanding Block Diagram Images by Generating Natural Language Descriptors](https://aclanthology.org/2022.findings-aacl.15/) *— 2022 · Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022*
58. [Control Prefixes for Parameter-Efficient Text Generation](https://aclanthology.org/2022.gem-1.31/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
59. [Deep Learning for Text Style Transfer: A Survey](https://aclanthology.org/2022.cl-1.6/) *— 2022 · Computational Linguistics*
60. [DeepStruct: Pretraining of Language Models for Structure Prediction](https://aclanthology.org/2022.findings-acl.67/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
61. [Fine-Grained Controllable Text Generation Using Non-Residual Prompting](https://aclanthology.org/2022.acl-long.471/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
62. [FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation](https://aclanthology.org/2022.acl-long.93/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
63. [GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation](https://aclanthology.org/2022.coling-1.506/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
64. [GEMv2: Multilingual NLG Benchmarking in a Single Line of Code](https://aclanthology.org/2022.emnlp-demos.27/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
65. [Generating Biographies on Wikipedia: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies](https://aclanthology.org/2022.acl-long.586/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
66. [Generating Questions from Wikidata Triples](https://aclanthology.org/2022.lrec-1.29/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
67. [Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation](https://aclanthology.org/2022.findings-emnlp.547/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
68. [Holistic Evaluation of Language Models](https://arxiv.org/abs/2211.09110) *— 2022 · arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023*
69. [MReD: A Meta-Review Dataset for Structure-Controllable Text Generation](https://aclanthology.org/2022.findings-acl.198/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
70. [Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation](https://aclanthology.org/2022.tacl-1.72/) *— 2022 · Transactions of the Association for Computational Linguistics*
71. [Multi-task Active Learning for Pre-trained Transformer-based Models](https://aclanthology.org/2022.tacl-1.70/) *— 2022 · Transactions of the Association for Computational Linguistics*
72. [Neural Pipeline for Zero-Shot Data-to-Text Generation](https://aclanthology.org/2022.acl-long.271/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
73. [Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis](https://aclanthology.org/2022.findings-emnlp.489/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
74. [Open Domain Question Answering with A Unified Knowledge Interface](https://aclanthology.org/2022.acl-long.113/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
75. [OpenPrompt: An Open-source Framework for Prompt-learning](https://aclanthology.org/2022.acl-demo.10/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations*
76. [Pre-training to Match for Unified Low-shot Relation Extraction](https://aclanthology.org/2022.acl-long.397/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
77. [Product Answer Generation from Heterogeneous Sources: A New Benchmark and Best Practices](https://aclanthology.org/2022.ecnlp-1.13/) *— 2022 · Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)*
78. [R2D2: Robust Data-to-Text with Replacement Detection](https://aclanthology.org/2022.emnlp-main.464/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
79. [Self-supervised Graph Masking Pre-training for Graph-to-Text Generation](https://aclanthology.org/2022.emnlp-main.321/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
80. [Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency](https://aclanthology.org/2022.findings-naacl.95/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
81. [Text-to-Text Extraction and Verbalization of Biomedical Event Graphs](https://aclanthology.org/2022.coling-1.238/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
82. [What Makes Data-to-Text Generation Hard for Pretrained Language Models?](https://aclanthology.org/2022.gem-1.50/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
83. [A Survey of Data Augmentation Approaches for NLP](https://arxiv.org/abs/2105.03075) *— 2021 · arXiv:2105.03075*
84. [AggGen: Ordering and Aggregating while Generating](https://aclanthology.org/2021.acl-long.113/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
85. [Biomedical Data-to-Text Generation via Fine-Tuning Transformers](https://aclanthology.org/2021.inlg-1.40/) *— 2021 · Proceedings of the 14th International Conference on Natural Language Generation*
86. [Building Adaptive Acceptability Classifiers for Neural NLG](https://aclanthology.org/2021.emnlp-main.53/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
87. [DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation](https://aclanthology.org/2021.acl-long.501/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
88. [Evaluating Text Generation from Discourse Representation Structures](https://md.mova.elamur.ai/free/en/docs/papers/wang-2021-evaluating-text-generation-discourse-representation/) *— our card · 2021 · Proceedings of the First Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
89. [Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models](https://aclanthology.org/2021.findings-acl.136/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
90. [Finetuned Language Models Are Zero-Shot Learners](https://arxiv.org/abs/2109.01652) *— 2021 · arXiv:2109.01652*
91. [Focused Attention Improves Document-Grounded Generation](https://aclanthology.org/2021.naacl-main.338/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
92. [Investigating Pretrained Language Models for Graph-to-Text Generation](https://aclanthology.org/2021.nlp4convai-1.20/) *— 2021 · Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI*
93. [JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs](https://aclanthology.org/2021.findings-acl.223/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
94. [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) *— 2021 · arXiv:2106.09685*
95. [Logic-Consistency Text Generation from Semantic Parses](https://aclanthology.org/2021.findings-acl.388/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
96. [Mention Flags (MF): Constraining Transformer-based Text Generators](https://aclanthology.org/2021.acl-long.9/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
97. [Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs](https://aclanthology.org/2021.textgraphs-1.2/) *— 2021 · Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)*
98. [Neural Data-to-Text Generation with LM-based Text Augmentation](https://aclanthology.org/2021.eacl-main.64/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
99. [Plan-then-Generate: Controlled Data-to-Text Generation via Planning](https://aclanthology.org/2021.findings-emnlp.76/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
100. [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://aclanthology.org/2021.acl-long.353/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*

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