---
title: "Who cites Melissa Roemmele — 151 papers of our library"
description: "151 papers of the Elamur Research library cite the works of Melissa Roemmele, found in their bibliographies; the most cited first."
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---
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# Who cites Melissa Roemmele

151 citations of their works from 151 papers of our library (of the 149,439 whose bibliographies we read) · 33 with a page here, the most cited first · [← Melissa Roemmele](https://md.research.elamur.ai/free/en/authors/melissa-roemmele/)

1. **[Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sewon Min, Xinxi Lyu, Ari Holtzman, et al.*2022 · 1,444 citations*
2. **[It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/schick-2021-it-s-not-just-size/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Timo Schick, Hinrich Schütze*2021 · 1,132 citations*
3. **[IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages](https://md.research.elamur.ai/free/en/papers/kakwani-2020-indicnlpsuite-monolingual-corpora-evaluation-benchmarks/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, et al.*2020 · 732 citations*
4. [**BloombergGPT: A Large Language Model for Finance arXiv:2303.17564**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 618 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
5. [**Few-shot Learning with Multilingual Language Models arXiv:2112.10668**Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, et al.*2021 · 500 citations*](https://md.research.elamur.ai/free/en/papers/lin-2021-few-shot-learning-multilingual-language/)
6. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts arXiv:2112.06905**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 496 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
7. [**Language Models are Multilingual Chain-of-Thought Reasoners arXiv:2210.03057**Freda Shi, Mirac Suzgun, Markus Freitag, et al.*2022 · 494 citations*](https://md.research.elamur.ai/free/en/papers/shi-2022-language-models-multilingual-chain-thought/)
8. [**The Falcon Series of Open Language Models arXiv:2311.16867**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 443 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/)
9. **[CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://md.research.elamur.ai/free/en/papers/ye-2021-crossfit-few-shot-learning-challenge/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qinyuan Ye, Bill Yuchen Lin, Xiang Ren*2021 · 210 citations*
10. **[Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time](https://md.research.elamur.ai/free/en/papers/liu-2023-deja-vu-contextual-sparsity-efficient/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Zichang Liu, Jue Wang, Tri Dao, et al.*2023 · 185 citations*
11. **[An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels](https://md.research.elamur.ai/free/en/papers/sorensen-2022-information-theoretic-approach-prompt-engineering/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Taylor Sorensen, Joshua Robinson, Christopher Rytting, et al.*2022 · 180 citations*
12. [**Unifying Human and Statistical Evaluation for Natural Language Generation arXiv:1904.02792**Tatsunori B. Hashimoto, Hugh Zhang, Percy Liang*2019 · 143 citations*](https://md.research.elamur.ai/free/en/papers/hashimoto-2019-unifying-human-statistical-evaluation-natural/)
13. **[What to Pre-Train on? Efficient Intermediate Task Selection](https://md.research.elamur.ai/free/en/papers/poth-2021-what-pre-train-efficient-intermediate/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Clifton Poth, Jonas Pfeiffer, Andreas Rücklé, et al.*2021 · 132 citations*
14. [**Knowledge Unlearning for Mitigating Privacy Risks in Language Models arXiv:2210.01504**Joel Jang, Dongkeun Yoon, Sohee Yang, et al.*2022 · 131 citations*](https://md.research.elamur.ai/free/en/papers/jang-2022-knowledge-unlearning-mitigating-privacy-risks/)
15. **[Improved Natural Language Generation via Loss Truncation](https://md.research.elamur.ai/free/en/papers/kang-2020-improved-natural-language-generation-loss/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Daniel Kang, Tatsunori B. Hashimoto*2020 · 130 citations*
16. [**PolyLM: An Open Source Polyglot Large Language Model arXiv:2307.06018**Xiangpeng Wei, Haoran Wei, Huan Lin, et al.*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-polylm-open-source-polyglot-large/)
17. **[Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate](https://md.research.elamur.ai/free/en/papers/xiong-2023-examining-inter-consistency-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kai Xiong, Xiao Ding, Yixin Cao, et al.*2023 · 94 citations*
18. **[Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://md.research.elamur.ai/free/en/papers/chan-2024-exploring-potential-chatgpt-sentence-level/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chunkit Chan, Cheng Jiayang, Weiqi Wang, et al.*2024 · 92 citations*
19. [**What Language Model to Train if You Have One Million GPU Hours? arXiv:2210.15424**Teven Le Scao, Thomas Wang, Daniel Hesslow, et al.*2022 · 73 citations*](https://md.research.elamur.ai/free/en/papers/scao-2022-language-model-train-if-have/)
20. **[Culturally Aware Natural Language Inference](https://md.research.elamur.ai/free/en/papers/huang-2023-culturally-aware-natural-language-inference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jing Huang, Diyi Yang*2023 · 61 citations*
21. **[Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic](https://md.research.elamur.ai/free/en/papers/bhardwaj-2024-language-models-are-homer-simpson/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Rishabh Bhardwaj, Duc Anh Do, Soujanya Poria*2024 · 59 citations*
22. [**Through the Lens of Core Competency: Survey on Evaluation of Large Language Models arXiv:2308.07902**Ziyu Zhuang, Qiguang Chen, Longxuan Ma, et al.*2023 · 35 citations*](https://md.research.elamur.ai/free/en/papers/zhuang-2023-through-lens-core-competency-survey/)
23. **[IberoBench: A Benchmark for LLM Evaluation in Iberian Languages](https://md.research.elamur.ai/free/en/papers/baucells-2025-iberobench-benchmark-llm-evaluation-iberian/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Irene Baucells, Javier Aula-Blasco, Iria de-Dios-Flores, et al.*2025 · 24 citations*
24. **[Culturally-Nuanced Story Generation for Reasoning in Low-Resource Languages: The Case of Javanese and Sundanese](https://md.research.elamur.ai/free/en/papers/pranida-2025-culturally-nuanced-story-generation-reasoning/) [Workshop on Multilingual Representation Learning](https://md.research.elamur.ai/free/en/venues/mrl/)**Salsabila Zahirah Pranida, Rifo Ahmad Genadi, Fajri Koto*2025 · 8 citations*
25. **[Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning](https://md.research.elamur.ai/free/en/papers/zhang-2026-beyond-random-sampling-efficient-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yang Zhang, Amr Mohamed, Hadi Abdine, et al.*2026 · 7 citations*
26. **[Comparing Human and Large Language Model Interpretation of Implicit Information](https://md.research.elamur.ai/free/en/papers/santis-2026-comparing-human-large-language-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Antonio De Santis, Tommaso Bonetti, Andrea Tocchetti, et al.*2026 · 2 citations*
27. **[Deputy: Accelerating Large Language Model Inference with Dynamic Low-Rank Substitution](https://md.research.elamur.ai/free/en/papers/zhou-2026-deputy-accelerating-large-language-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuhua Zhou, Shichao Weng, Changhai Zhou, et al.*2026 · 1 citation*
28. **[Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks](https://md.research.elamur.ai/free/en/papers/yamaguchi-2026-enhancing-linguistic-competence-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Atsuki Yamaguchi, Maggie Mi, Nikolaos Aletras*2026 · 1 citation*
29. **[Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection](https://md.research.elamur.ai/free/en/papers/stacey-2026-improving-ood-performance-closed-source/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Joe Stacey, Lisa Alazraki, Aran Ubhi, et al.*2026 · 1 citation*
30. **[QRAFT: QLoRA Retrieval-Augmented Fine-Tuning for Causal Span Extraction in Financial Documents](https://md.research.elamur.ai/free/en/papers/sarda-2026-qraft-qlora-retrieval-augmented-fine/) [Financial Narrative Processing Workshop](https://md.research.elamur.ai/free/en/venues/fnp/)**Bavya Sarda, Pulkit Chatwal, Sonal Dabral*2026 · 1 citation*
31. **[Can a Remedy Find a Researcher? Exploring the Development of Semantic Knowledge in Italian BabyLMs](https://md.research.elamur.ai/free/en/papers/suozzi-2026-can-remedy-find-researcher-exploring/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Alice Suozzi, Luca Capone, Gianluca Lebani, et al.*2026*
32. **[Memory Dial: A Training Framework for Controllable Memorization in Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-memory-dial-training-framework-controllable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiangbo Zhang, Ali Emami*2026*
33. [**The Validity of Coreference-based Evaluations of Natural Language Understanding arXiv:2602.16200**Ian Porada*2026*](https://md.research.elamur.ai/free/en/papers/porada-2026-validity-coreference-based-evaluations-natural/)

118 more citing papers of our library have no page here yet.

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