---
title: "Who cites Nils Holzenberger — 232 papers of our library"
description: "232 papers of the Elamur Research library cite the works of Nils Holzenberger, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/nils-holzenberger/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/nils-holzenberger/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Nils Holzenberger](https://md.research.elamur.ai/free/en/authors/nils-holzenberger/) / cited by

# Who cites Nils Holzenberger

288 citations of their works from 232 papers of our library (of the 149,439 whose bibliographies we read) · 32 with a page here, the most cited first · [← Nils Holzenberger](https://md.research.elamur.ai/free/en/authors/nils-holzenberger/)

1. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
2. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
3. [**BARTScore: Evaluating Generated Text as Text Generation arXiv:2106.11520**Weizhe Yuan, Graham Neubig, Pengfei Liu*2021 · 815 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2021-bartscore-evaluating-generated-text-text/)
4. [**Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models arXiv:2401.01301 · Journal of Legal Analysis 16, no. 1 (2024): 64-93**Matthew Dahl, Varun Magesh, Mirac Suzgun, et al.*2024 · 437 citations*](https://md.research.elamur.ai/free/en/papers/dahl-2024-large-legal-fictions-profiling-legal/)
5. [**LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models arXiv:2308.11462**Neel Guha; Julian Nyarko; Daniel E. Ho; Christopher Re; Adam Chilton; Aditya Narayana; Alex Chohlas-Wood; Austin Peters; Brandon Waldon; Daniel N. Rockmore; Diego Zambrano; Dmitry Talisman; Enam Hoque; Faiz Surani; Frank Fagan; Galit Sarfaty; Gregory M. Dickinson; Haggai Porat; Jason Hegland; Jessica Wu; Joe Nudell; Joel Niklaus; John Nay; Jonathan H. Choi; Kevin Tobia; Margaret Hagan; Megan Ma; Michael Livermore; Nikon Rasumov-Rahe; Nils Holzenberger; Noam Kolt; Peter Henderson; Sean Rehaag; Sharad Goel; Shang Gao; Spencer Williams; Sunny Gandhi; Tom Zur; Varun Iyer;, Zehua Li*2023 · 389 citations*](https://md.research.elamur.ai/free/en/papers/guha-2023-legalbench-collaboratively-built-benchmark-measuring/)
6. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
7. [**Is ChatGPT a Good NLG Evaluator? A Preliminary Study arXiv:2303.04048**Jiaan Wang, Yunlong Liang, Fandong Meng, et al.*2023 · 287 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-chatgpt-good-nlg-evaluator-preliminary/)
8. **[TRUE: Re-evaluating Factual Consistency Evaluation](https://md.research.elamur.ai/free/en/papers/honovich-2022-true-re-evaluating-factual-consistency/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Or Honovich, Roee Aharoni, Jonathan Herzig, et al.*2022 · 279 citations*
9. [**CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review arXiv:2103.06268**Dan Hendrycks, Collin Burns, Anya Chen, et al.*2021 · 268 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-cuad-expert-annotated-nlp-dataset/)
10. **[Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing](https://md.research.elamur.ai/free/en/papers/thompson-2020-automatic-machine-translation-evaluation-many/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Brian Thompson, Matt Post*2020 · 260 citations*
11. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
12. **[ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts](https://md.research.elamur.ai/free/en/papers/koreeda-2021-contractnli-dataset-document-level-natural/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuta Koreeda, Christopher Manning*2021 · 177 citations*
13. **[Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/louis-2023-interpretable-long-form-legal-question/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Antoine Louis, Gijs van Dijck, Gerasimos Spanakis*2023 · 157 citations*
14. [**Can GPT-3 Perform Statutory Reasoning? arXiv:2302.06100**Andrew Blair-Stanek, Nils Holzenberger, Benjamin Van Durme*2023 · 154 citations*](https://md.research.elamur.ai/free/en/papers/blair-stanek-2023-gpt-3-perform-statutory-reasoning/)
15. **[Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions](https://md.research.elamur.ai/free/en/papers/chen-2025-benchmarking-large-language-models-answering/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hanjie Chen, Zhouxiang Fang, Yash Singla, et al.*2025 · 75 citations*
16. [**A Reasoning-Focused Legal Retrieval Benchmark arXiv:2505.03970**Lucia Zheng, Neel Guha, Javokhir Arifov, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2025-reasoning-focused-legal-retrieval-benchmark/)
17. **[Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity](https://md.research.elamur.ai/free/en/papers/thompson-2020-paraphrase-generation-as-zero-shot/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Brian Thompson, Matt Post*2020 · 55 citations*
18. **[“According to . . . “: Prompting Language Models Improves Quoting from Pre-Training Data](https://md.research.elamur.ai/free/en/papers/weller-2024-according-prompting-language-models-improves/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Orion Weller, Marc Marone, Nathaniel Weir, et al.*2024 · 46 citations*
19. [**Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing arXiv:2305.16635**Jaehun Jung, Peter West, Liwei Jiang, et al.*2023 · 36 citations*](https://md.research.elamur.ai/free/en/papers/jung-2023-impossible-distillation-low-quality-model/)
20. **[Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models](https://md.research.elamur.ai/free/en/papers/kim-2024-developing-pragmatic-benchmark-assessing-korean/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yeeun Kim, Youngrok Choi, Eunkyung Choi, et al.*2024 · 24 citations*
21. [**ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation**Kuan-Hao Huang, Varun Iyer, I-Hung Hsu, et al.*2023 · 24 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-paraamr-large-scale-syntactically-diverse/)
22. **[Pragmatic Inference Chain (PIC) Improving LLMs' Reasoning of Authentic Implicit Toxic Language](https://md.research.elamur.ai/free/en/papers/chen-2025-pragmatic-inference-chain-pic-improving/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xi Chen, Shuo Wang*2025 · 9 citations*
23. **[JuriFindIT: an Italian legal retrieval dataset](https://md.research.elamur.ai/free/en/papers/noce-2026-jurifindit-italian-legal-retrieval-dataset/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Niko Dalla Noce, Davide Colla, Sina Farhang Doust, et al.*2026 · 3 citations*
24. **[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*
25. **[OpenExempt: A Diagnostic Benchmark for Legal Reasoning and a Framework for Creating Custom Benchmarks on Demand](https://md.research.elamur.ai/free/en/papers/servantez-2026-openexempt-diagnostic-benchmark-legal-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sergio Servantez, Sarah B. Lawsky, Rajiv Jain, et al.*2026 · 2 citations*
26. **[CLASE: A Hybrid Method for Chinese Legalese Stylistic Evaluation](https://md.research.elamur.ai/free/en/papers/ma-2026-clase-hybrid-method-chinese-legalese/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yiran Rex Ma, Yuxiao Ye, Huiyuan Xie*2026 · 1 citation*
27. **[bLLeQA: Benchmarking LLMs for Grounded Legal Question-Answering in French and Dutch](https://md.research.elamur.ai/free/en/papers/banar-2026-blleqa-benchmarking-llms-grounded-legal/) [Workshop on Towards Knowledgeable Foundation Models](https://md.research.elamur.ai/free/en/venues/knowfm/)**Nikolay Banar, Ehsan Lotfi, Jens Van Nooten, et al.*2026*
28. [**Classifying Interpretive Canons at the Sentence Level: A Benchmark from the German Federal Constitutional Court arXiv:2609.26945**Felix Ringe*2026*](https://md.research.elamur.ai/free/en/papers/ringe-2026-classifying-interpretive-canons-sentence-level/)
29. **[Paraphrasing as Zero-shot Translation with Feature-guided Diversity Enhancement](https://md.research.elamur.ai/free/en/papers/yan-2026-paraphrasing-as-zero-shot-translation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziyue Yan, Hongying Zan, Xinglin Lyu, et al.*2026*
30. [**SteuerLLM: Local specialized large language model for German tax law analysis arXiv:2602.11081 · Scientific Reports, Volume 16, 2026**Sebastian Wind, Jeta Sopa, Laurin Schmid, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wind-2026-steuerllm-local-specialized-large-language/)
31. **[Structured Legal Document Generation in India: A Model-Agnostic Wrapper Approach with VidhikDastaavej](https://md.research.elamur.ai/free/en/papers/nigam-2026-structured-legal-document-generation-india/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Shubham Kumar Nigam, Deepak Patnaik Balaramamahanthi, Noel Shallum, et al.*2026*
32. **[Aligning Language Models for Icelandic Legal Text Summarization](https://md.research.elamur.ai/free/en/papers/dharson-2025-aligning-language-models-icelandic-legal/) [Nordic Conference on Computational Linguistics (NoDaLiDa)](https://md.research.elamur.ai/free/en/venues/nodalida/)**Þórir Hrafn Harðarson, Hrafn Loftsson, Stefán Ólafsson*2025*

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

- [← back to](https://md.research.elamur.ai/free/en/authors/nils-holzenberger/) — Nils Holzenberger