---
title: "Who cites Daniel Preoţiuc-Pietro — 254 papers of our library"
description: "254 papers of the Elamur Research library cite the works of Daniel Preoţiuc-Pietro, found in their bibliographies; the most cited first."
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---
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# Who cites Daniel Preoţiuc-Pietro

254 citations of their works from 254 papers of our library (of the 149,439 whose bibliographies we read) · 37 with a page here, the most cited first · [← Daniel Preoţiuc-Pietro](https://md.research.elamur.ai/free/en/authors/daniel-preotiuc-pietro/)

1. **[A general approach for predicting the behavior of the Supreme Court of the United States](https://md.research.elamur.ai/free/en/papers/katz-2017-general-approach-predicting-behavior/) [PLoS ONE](https://md.research.elamur.ai/free/en/venues/plos-one/)**Daniel Martin Katz, Michael J. Bommarito, Josh Blackman*2017 · 598 citations*
2. **[Neural Legal Judgment Prediction in English](https://md.research.elamur.ai/free/en/papers/chalkidis-2019-neural-legal-judgment-prediction-english/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ilias Chalkidis, Ion Androutsopoulos, Nikolaos Aletras*2019 · 559 citations*
3. **[How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://md.research.elamur.ai/free/en/papers/zhong-2020-how-does-nlp-benefit-legal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoxi Zhong, Chaojun Xiao, Cunchao Tu, et al.*2020 · 518 citations*
4. **[Legal Judgment Prediction via Topological Learning](https://md.research.elamur.ai/free/en/papers/zhong-2018-legal-judgment-prediction-topological-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Haoxi Zhong, Zhipeng Guo, Cunchao Tu, et al.*2018 · 480 citations*
5. **[BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://md.research.elamur.ai/free/en/papers/labrak-2024-biomistral-collection-open-source-pretrained/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yanis Labrak, Adrien Bazoge, Emmanuel Morin, et al.*2024 · 409 citations*
6. [**CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction arXiv:1807.02478**Chaojun Xiao, Haoxi Zhong, Zhipeng Guo, et al.*2018 · 375 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2018-cail2018-large-scale-legal-dataset/)
7. **[TIES-Merging: Resolving Interference When Merging Models](https://md.research.elamur.ai/free/en/papers/yadav-2023-ties-merging-resolving-interference-merging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Prateek Yadav, Derek Tam, Leshem Choshen, et al.*2023 · 324 citations*
8. **[Large-Scale Multi-Label Text Classification on EU Legislation](https://md.research.elamur.ai/free/en/papers/chalkidis-2019-large-scale-multi-label-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ilias Chalkidis, Emmanouil Fergadiotis, Prodromos Malakasiotis, et al.*2019 · 283 citations*
9. [**LexGLUE: A Benchmark Dataset for Legal Language Understanding in English arXiv:2110.00976**Ilias Chalkidis; Abhik Jana; Dirk Hartung; Michael Bommarito; Ion Androutsopoulos; Daniel Martin Katz;, Nikolaos Aletras*2021 · 258 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-lexglue-benchmark-dataset-legal-language/)
10. **[ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation](https://md.research.elamur.ai/free/en/papers/malik-2021-ildc-cjpe-indian-legal-documents/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vijit Malik, Rishabh Sanjay, Shubham Kumar Nigam, et al.*2021 · 251 citations*
11. [**CLAUDETTE: an Automated Detector of Potentially Unfair Clauses in Online Terms of Service arXiv:1805.01217 · Artif. Intell. Law 27 (2019) 117-139**Marco Lippi, Przemyslaw Palka, Giuseppe Contissa, et al.*2018 · 246 citations*](https://md.research.elamur.ai/free/en/papers/lippi-2018-claudette-automated-detector-potentially-unfair/)
12. **[LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://md.research.elamur.ai/free/en/papers/chalkidis-2022-lexglue-benchmark-dataset-legal-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ilias Chalkidis, Abhik Jana, Dirk Hartung, et al.*2022 · 239 citations*
13. [**Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch arXiv:2311.03099**Le Yu, Bowen Yu, Haiyang Yu, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-language-models-super-mario-absorbing/)
14. **[Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-paragraph-level-rationale-extraction-through/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ilias Chalkidis, Manos Fergadiotis, Dimitrios Tsarapatsanis, et al.*2021 · 168 citations*
15. **[MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-multieurlex-multi-lingual-multi-label/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ilias Chalkidis, Manos Fergadiotis, Ion Androutsopoulos*2021 · 160 citations*
16. [**When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset arXiv:2104.08671**Lucia Zheng, Neel Guha, Brandon R. Anderson, et al.*2021 · 134 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2021-pretraining-help-assessing-self-supervised/)
17. **[Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark](https://md.research.elamur.ai/free/en/papers/niklaus-2021-swiss-judgment-prediction-multilingual-legal/) [Natural Legal Language Processing Workshop](https://md.research.elamur.ai/free/en/venues/nllp/)**Joel Niklaus, Ilias Chalkidis, Matthias Stürmer*2021 · 129 citations*
18. [**SaulLM-7B: A pioneering Large Language Model for Law arXiv:2403.03883**Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/colombo-2024-saullm-7b-pioneering-large-language/)
19. [**LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition arXiv:2307.13269**Chengsong Huang, Qian Liu, Bill Yuchen Lin, et al.*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-lorahub-efficient-cross-task-generalization/)
20. [**Arcee's MergeKit: A Toolkit for Merging Large Language Models arXiv:2403.13257**Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi, et al.*2024 · 105 citations*](https://md.research.elamur.ai/free/en/papers/goddard-2024-arcee-s-mergekit-toolkit-merging/)
21. **[LLMs Are Few-Shot In-Context Low-Resource Language Learners](https://md.research.elamur.ai/free/en/papers/cahyawijaya-2024-llms-are-few-shot-context/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Samuel Cahyawijaya, Holy Lovenia, Pascale Fung*2024 · 93 citations*
22. [**Knowledge Fusion of Large Language Models arXiv:2401.10491**Fanqi Wan, Xinting Huang, Deng Cai, et al.*2024 · 82 citations*](https://md.research.elamur.ai/free/en/papers/wan-2024-knowledge-fusion-large-language-models/)
23. [**Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities arXiv:2408.07666**Enneng Yang, Li Shen, Guibing Guo, et al.*2024 · 79 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-model-merging-llms-mllms-methods/)
24. [**AdaMerging: Adaptive Model Merging for Multi-Task Learning arXiv:2310.02575**Enneng Yang, Zhenyi Wang, Li Shen, et al.*2023 · 59 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-adamerging-adaptive-model-merging-multi/)
25. [**Dialect prejudice predicts AI decisions about people's character, employability, and criminality arXiv:2403.00742**Valentin Hofmann, Pratyusha Ria Kalluri, Dan Jurafsky, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/hofmann-2024-dialect-prejudice-predicts-ai-decisions/)
26. **[Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts](https://md.research.elamur.ai/free/en/papers/tan-2024-personalized-pieces-efficient-personalized-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhaoxuan Tan, Zheyuan Liu, Meng Jiang*2024 · 40 citations*
27. **[FuseChat: Knowledge Fusion of Chat Models](https://md.research.elamur.ai/free/en/papers/wan-2025-fusechat-knowledge-fusion-chat-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fanqi Wan, Longguang Zhong, Ziyi Yang, et al.*2025 · 25 citations*
28. **[CUTE: A Multilingual Dataset for Enhancing Cross-Lingual Knowledge Transfer in Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/zhuang-2025-cute-multilingual-dataset-enhancing-cross/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Wenhao Zhuang, Yuan Sun*2025 · 7 citations*
29. **[LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-Disjoint](https://md.research.elamur.ai/free/en/papers/ma-2025-led-merging-mitigating-safety-utility/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qianli Ma, Dongrui Liu, Qian Chen, et al.*2025 · 5 citations*
30. [**ALJP: An Arabic Legal Judgment Prediction in Personal Status Cases Using Machine Learning Models arXiv:2309.00238**Salwa Abbara, Mona Hafez, Aya Kazzaz, et al.*2023 · 4 citations*](https://md.research.elamur.ai/free/en/papers/abbara-2023-aljp-arabic-legal-judgment-prediction/)
31. **[Accurate Legal Reasoning at Scale: Neuro-Symbolic Offloading and Structural Auditability for Robust Legal Adjudication](https://md.research.elamur.ai/free/en/papers/sojka-2026-accurate-legal-reasoning-scale-neuro/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Stanisław Sójka, Witold Kowalczyk*2026 · 2 citations*
32. **[Why Do More Experts Fail? A Theoretical Analysis of Model Merging](https://md.research.elamur.ai/free/en/papers/wang-2026-why-do-more-experts-fail/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zijing Wang, Xingle Xu, YongKang Liu, et al.*2026 · 2 citations*
33. **[H3Fusion: Helpful, Harmless, Honest Fusion of Aligned LLMs](https://md.research.elamur.ai/free/en/papers/tekin-2026-h3fusion-helpful-harmless-honest-fusion/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Selim Furkan Tekin, Fatih Ilhan, Sihao Hu, et al.*2026 · 1 citation*
34. **[DivMerge: A divergence-based model merging method for multi-tasking](https://md.research.elamur.ai/free/en/papers/touayouch-2026-divmerge-divergence-based-model-merging/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Brahim Touayouch, Loïc Fosse, Géraldine Damnati, et al.*2026*
35. **[Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters](https://md.research.elamur.ai/free/en/papers/xu-2026-investigating-cross-modal-skill-injection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyu Xu, Lean Wang, Yuanxin Liu, et al.*2026*
36. **[On the Representation Geometry of LoRA Model Merging](https://md.research.elamur.ai/free/en/papers/lu-2026-representation-geometry-lora-model-merging/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenyang Lu, Jiaru Li, Jinman Zhao, et al.*2026*
37. **[ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging](https://md.research.elamur.ai/free/en/papers/yang-2026-reasonany-incorporating-reasoning-capability-any/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyao Yang, Chen Qian, Wen Shen, et al.*2026*

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

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