🔗 Who cites «Generate, Annotate, and Learn: NLP with Synthetic Text»

31 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · ← back to «Generate, Annotate, and Learn: NLP with Synthetic Text»

1. Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMsarXiv:2402.12030 · Transactions on Machine Learning Research, January 2025Nicolas Boizard, Kevin El Haddad, Celine Hudelot, et al.2024 · 35 citations
2. Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and ChallengesarXiv:2309.12426Vinay Samuel, Houda Aynaou, Arijit Ghosh Chowdhury, et al.2023 · 16 citations
3. AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval AlignmentInternational Conference on Computational Linguistics (COLING)Yilong Lai, Jialong Wu, Congzhi Zhang, et al.2025 · 9 citations
4. A Novel Synthetic Dataset for Few-Shot Legal Relation Extraction in GermanLanguage Resources and Evaluation Conference (LREC)Shiva Banasaz Nouri, Elena Leitner, Julian Moreno-Schneider, et al.2026
5. LingVarBench: Benchmarking LLMs on Entity Recognitions and Linguistic Verbalization Patterns in Phone-Call TranscriptsConference of the European Chapter of the Association for Computational Linguistics (EACL)Seyedali Mohammadi, Manas Paldhe, Amit Chhabra, et al.2026
6. When Efficiency Meets Safety: A Benchmark Security Analysis of KV Cache Compression in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaoxiao Ma, Kuofeng Gao, Zeyi Lu, et al.2026

25 more papers of our library cite it; they have no page here yet.

This page in Markdown → https://md.research.elamur.ai/free/en/papers/he-2022-generate-annotate-learn-nlp-synthetic/cited-by/