---
title: "Who cites Jingwei Ni — 113 papers of our library"
description: "113 papers of the Elamur Research library cite the works of Jingwei Ni, found in their bibliographies; the most cited first."
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---
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# Who cites Jingwei Ni

114 citations of their works from 113 papers of our library (of the 149,439 whose bibliographies we read) · 29 with a page here, the most cited first · [← Jingwei Ni](https://md.research.elamur.ai/free/en/authors/jingwei-ni/)

1. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 130 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
2. **[Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization](https://md.research.elamur.ai/free/en/papers/foroutan-2026-parity-aware-byte-pair-encoding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Negar Foroutan, Clara Meister, Debjit Paul, et al.*2026 · 12 citations*
3. **[PRBench: Large-Scale Expert Rubrics for Evaluating High-Stakes Professional Reasoning](https://md.research.elamur.ai/free/en/papers/akyurek-2026-prbench-large-scale-expert-rubrics/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Afra Feyza Akyürek, Advait Gosai, Chen Bo Calvin Zhang, et al.*2026 · 7 citations*
4. **[Estonian WinoGrande Dataset: Comparative Analysis of LLM Performance on Human and Machine Translation](https://md.research.elamur.ai/free/en/papers/ojastu-2026-estonian-winogrande-dataset-comparative-analysis/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Marii Ojastu, Hele-Andra Kuulmets, Aleksei Dorkin, et al.*2026 · 3 citations*
5. **[From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms](https://md.research.elamur.ai/free/en/papers/luo-2026-storage-experience-survey-evolution-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinghao Luo, Yuchen Tian, Chuxue Cao, et al.*2026 · 3 citations*
6. **[Korean Canonical Legal Benchmark: Toward Knowledge-Independent Evaluation of LLMs' Legal Reasoning Capabilities](https://md.research.elamur.ai/free/en/papers/oh-2026-korean-canonical-legal-benchmark-knowledge/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Hongseok Oh, Wonseok Hwang, Kyoung-Woon On*2026 · 3 citations*
7. [**A Sovereign, Open-Source Foundation Model for German and English arXiv:2607.09424**Soofi-Team: Benedikt Droste, David Fitzek, Ruben Harle, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/droste-2026-sovereign-open-source-foundation-model/)
8. **[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*
9. **[ALBA: A European Portuguese Benchmark for Evaluating Language and Linguistic Dimensions in Generative LLMs](https://md.research.elamur.ai/free/en/papers/vieira-2026-alba-european-portuguese-benchmark-evaluating/) [International Conference on Computational Processing of Portuguese](https://md.research.elamur.ai/free/en/venues/propor/)**Inês Vieira, Inês Calvo, Iago Paulo, et al.*2026 · 2 citations*
10. **[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*
11. **[AMALIA: A Fully Open Large Language Model for European Portuguese](https://md.research.elamur.ai/free/en/papers/simplicio-2026-amalia-fully-open-large-language/) [International Conference on Computational Processing of Portuguese](https://md.research.elamur.ai/free/en/venues/propor/)**Afonso Simplício, Gonçalo Vinagre, Miguel Moura Ramos, et al.*2026 · 1 citation*
12. [**BenGER Platform: A Collaborative Web Platform for End-to-End Benchmarking of German Legal Tasks arXiv:2604.13583**Sebastian Nagl, Matthias Grabmair*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/nagl-2026-benger-platform-collaborative-web-platform/)
13. [**MÖVE: A Holistic LLM Benchmark for the German Public Sector arXiv:2606.13111**Camilla Dalerci, Thilo Michael, Robin Schaefer, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/dalerci-2026-m-ove-holistic-llm-benchmark/)
14. **[When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models](https://md.research.elamur.ai/free/en/papers/navneet-2026-ai-writes-whose-voice-remains/) [Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA)](https://md.research.elamur.ai/free/en/venues/extended-abstracts-of-the-chi-conference-on-human-factors-in-computing-systems/)**Satyam Kumar Navneet, Joydeep Chandra, Yong Zhang*2026 · 1 citation*
15. **[Who Endorsed It? Measuring Authority Bias Across Expertise Levels in Language Models](https://md.research.elamur.ai/free/en/papers/mammen-2026-who-endorsed-it-measuring-authority/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Priyanka Mary Mammen, Emil Joswin, Shankar Venkitachalam*2026 · 1 citation*
16. **[Are the LLMs Capable of Maintaining at Least the Language Genus?](https://md.research.elamur.ai/free/en/papers/mitrovic-2026-are-llms-capable-maintaining-least/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Sandra Mitrović, David Kletz, Ljiljana Dolamic, et al.*2026*
17. [**BenGER: Benchmarking LLM Systems on Subsumption-Based Legal Reasoning in German Law arXiv:2605.28183**Sebastian Nagl, Ann-Kristin Mayrhofer, Martin Heidebach, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nagl-2026-benger-benchmarking-llm-systems-subsumption/)
18. **[Can Large Language Models Replace Statistical Software?](https://md.research.elamur.ai/free/en/papers/staudt-2026-can-large-language-models-replace/) [Edition of the Swiss Text Analytics Conference](https://md.research.elamur.ai/free/en/venues/swisstext/)**Yves Staudt*2026*
19. **[EIFFEL: a novel benchmark to measure bias of English heavy training on French idiomatic expressions](https://md.research.elamur.ai/free/en/papers/noel-2026-eiffel-novel-benchmark-measure-bias/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Charlotte Noel, Nicholas Asher, Olivier Gouvert, et al.*2026*
20. [**GradeLegal: Automated Grading for German Legal Cases arXiv:2605.21076**Abdullah Al Zubaer, Lorenz Wendlinger, Simon Alexander Nonn, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zubaer-2026-gradelegal-automated-grading-german-legal/)
21. **[Graph-Augmented LLMs for Swiss MP Ideology Prediction](https://md.research.elamur.ai/free/en/papers/yuan-2026-graph-augmented-llms-swiss-mp/) [Edition of the Swiss Text Analytics Conference](https://md.research.elamur.ai/free/en/venues/swisstext/)**Yifei Yuan, Luis Salamanca, Sophia Schlosser, et al.*2026*
22. **[Is Biomedical Specialization Still Worth It? Insights from Domain-Adaptive Language Modelling with a New French Health Corpus](https://md.research.elamur.ai/free/en/papers/mannion-2026-is-biomedical-specialization-still-worth/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Aidan Mannion, Cécile Macaire, Armand Violle, et al.*2026*
23. [**LuxSQA: Ask Me in Luxembourgish with TTS-Augmented Spoken Question Answering arXiv:2607.02763**Nina Hosseini-Kivanani, Marco Matassoni, Alessio Brutti*2026*](https://md.research.elamur.ai/free/en/papers/hosseini-kivanani-2026-luxsqa-ask-me-luxembourgish-tts/)
24. **[Par-ITA: Benchmarking Seq2Seq and LLMs on a Human-Supervised Parallel Corpus for Italian Hyperpartisan Neutralization](https://md.research.elamur.ai/free/en/papers/maggini-2026-par-ita-benchmarking-seq2seq-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Michele Joshua Maggini, Søren Fomsgaard, Michele Maestroni, et al.*2026*
25. **[POS Tagging with Generative LLMs for Historical Germanic Low-Resource Languages: An Evaluation Against Fine-Tuned BERT](https://md.research.elamur.ai/free/en/papers/miani-2026-pos-tagging-generative-llms-historical/) [Workshop on Language Technologies for Historical and Ancient Languages @ LREC](https://md.research.elamur.ai/free/en/venues/lt4hala/)**Irene Miani, Gregory Darwin, Sara Stymne*2026*
26. [**Reading the News: Adapting Large Language Models to Swedish Journalism Through Continued Pre-Training arXiv:2608.30609**Lukas Borggren, Jenny Kunz, Marco Kuhlmann*2026*](https://md.research.elamur.ai/free/en/papers/borggren-2026-reading-news-adapting-large-language/)
27. **[Robust Language Identification for Romansh Varieties](https://md.research.elamur.ai/free/en/papers/model-2026-robust-language-identification-romansh-varieties/) [Edition of the Swiss Text Analytics Conference](https://md.research.elamur.ai/free/en/venues/swisstext/)**Charlotte Model, Sina Ahmadi, Jannis Vamvas*2026*
28. **[ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning](https://md.research.elamur.ai/free/en/papers/smirnov-2026-thinkbooster-unified-framework-seamless-test/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vladislav Smirnov, Quang-Chieu Nguyen, Sergey Senichev, et al.*2026*
29. **[Why do Large Language Models Fail in Low-resource Translation? Unraveling the Token Dynamics of Large Language Models for Machine Translation](https://md.research.elamur.ai/free/en/papers/qian-2026-why-do-large-language-models/) [Annual Conference of the European Association for Machine Translation (EAMT)](https://md.research.elamur.ai/free/en/venues/eamt/)**Shenbin Qian, Yves Scherrer*2026*

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

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