---
title: "Who cites Jinghui Lu — 81 papers of our library"
description: "81 papers of the Elamur Research library cite the works of Jinghui Lu, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/jinghui-lu/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/jinghui-lu/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Jinghui Lu](https://md.research.elamur.ai/free/en/authors/jinghui-lu/) / cited by

# Who cites Jinghui Lu

81 citations of their works from 81 papers of our library (of the 149,439 whose bibliographies we read) · 23 with a page here, the most cited first · [← Jinghui Lu](https://md.research.elamur.ai/free/en/authors/jinghui-lu/)

1. [**Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models arXiv:2503.16419**Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.*2025 · 157 citations*](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/)
2. [**Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning arXiv:2512.20848**NVIDIA: Aaron Blakeman, Aaron Grattafiori, Aarti Basant, et al.*2025 · 27 citations*](https://md.research.elamur.ai/free/en/papers/blakeman-2025-nemotron-3-nano-open-efficient/)
3. [**Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation arXiv:2603.19220**Zhuolin Yang, Zihan Liu, Yang Chen, et al.*2026 · 16 citations*](https://md.research.elamur.ai/free/en/papers/yang-2026-nemotron-cascade-2-post-training/)
4. **[MultiLoKo: a multilingual local knowledge benchmark for LLMs spanning 31 languages](https://md.research.elamur.ai/free/en/papers/hupkes-2025-multiloko-multilingual-local-knowledge-benchmark/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Dieuwke Hupkes, Nikolay Bogoychev*2025 · 12 citations*
5. [**100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models arXiv:2505.00551**Chong Zhang, Yue Deng, Xiang Lin, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-100-days-after-deepseek/)
6. **[IREASONER: Trajectory-Aware Intrinsic Reasoning Supervision for Self-Evolving Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/sunil-2026-ireasoner-trajectory-aware-intrinsic-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Meghana Sunil, Manikandarajan Venmathimaran, Muthu Subash Kavitha*2026 · 2 citations*
7. **[POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering](https://md.research.elamur.ai/free/en/papers/xu-2026-polychartqa-benchmarking-large-vision-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yichen Xu, Liangyu Chen, Liang Zhang, et al.*2026 · 2 citations*
8. **[XOXO: Stealthy Cross-Origin Context Poisoning Attacks against AI Coding Assistants](https://md.research.elamur.ai/free/en/papers/storek-2026-xoxo-stealthy-cross-origin-context/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Adam Štorek, Mukur Gupta, Noopur Bhatt, et al.*2026 · 2 citations*
9. **[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*
10. **[ARISE: An Adaptive Resolution-Aware Metric for Test-Time Scaling Evaluation in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/yin-2026-arise-adaptive-resolution-aware-metric/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhangyue Yin, Qiushi Sun, Zhiyuan Zeng, et al.*2026 · 1 citation*
11. **[Serbian SuperGLUE: Towards an Evaluation Benchmark for South Slavic Language Models](https://md.research.elamur.ai/free/en/papers/perovic-2026-serbian-superglue-evaluation-benchmark-south/) [Workshop on Language Models for Low-Resource Languages](https://md.research.elamur.ai/free/en/venues/loreslm/)**Mitar Perovic, Teodora Mihajlov*2026 · 1 citation*
12. **[A Comparative Study of Multilingual Fine-tuning and Prompting for Automatic Text Readability Classification in Galician](https://md.research.elamur.ai/free/en/papers/rey-2026-comparative-study-multilingual-fine-tuning/) [Joint Workshop on Readability and Text Simplification (READIxTSAR) @ LREC](https://md.research.elamur.ai/free/en/venues/readi/)**Sandra Rodríguez Rey, Marcos Garcia*2026*
13. **[Charting the European LLM Benchmarking Landscape: A New Taxonomy and Registry](https://md.research.elamur.ai/free/en/papers/vintar-2026-charting-european-llm-benchmarking-landscape/) [Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities @ LREC](https://md.research.elamur.ai/free/en/venues/llms4ssh/)**Spela Vintar, Mojca Brglez, Taja Kuzman Pungeršek, et al.*2026*
14. **[Expert Calibration Lens for Pruning Mixture of Experts](https://md.research.elamur.ai/free/en/papers/salim-2026-expert-calibration-lens-pruning-mixture/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luis Frentzen Salim, Chia-Chun Wu, Tran Van Nhiem, et al.*2026*
15. **[Generation of Instruction and Preference Dataset for Improving Japanese Instruction Following in LLMs](https://md.research.elamur.ai/free/en/papers/moriyama-2026-generation-instruction-preference-dataset-improving/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Kei Moriyama, Takashi Kodama, Kouta Nakayama*2026*
16. **[How to Improve LLMs' Performance on Specific Languages: A Perspective on LLM-Derived Language Similarity](https://md.research.elamur.ai/free/en/papers/shi-2026-how-improve-llms-performance-specific/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinhe Shi, Qingcheng Zeng, Weihao Xuan, et al.*2026*
17. **[I Understand How You Feel: Enhancing Deeper Emotional Support Through Multilingual Emotional Validation in Dialogue System](https://md.research.elamur.ai/free/en/papers/pang-2026-i-understand-how-you-feel/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Zi Haur Pang, Yahui Fu, Koji Inoue, et al.*2026*
18. **[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*
19. **[LLMs as annotators of credibility assessment in Danish asylum decisions: evaluating classification performance and errors beyond aggregated metrics](https://md.research.elamur.ai/free/en/papers/renaux-2026-llms-as-annotators-credibility-assessment/) [Linguistic Annotation Workshop (LAW)](https://md.research.elamur.ai/free/en/venues/law/)**Galadrielle Humblot-Renaux, Mohammad N. S. Jahromi, Rohat Bakuri-Jørgensen, et al.*2026*
20. **[mmPISA-bench: Do LLMs Reason Equally Well Across 43 Languages?](https://md.research.elamur.ai/free/en/papers/sapenov-2026-mmpisa-bench-do-llms-reason/) [Workshop on Multilinguality in the Era of Large Language Models](https://md.research.elamur.ai/free/en/venues/mellm/)**Yerzhan Sapenov, Jaromir Savelka*2026*
21. **[Multi-Task Learning Trade-offs in Vision–Language Models for Ancient Chinese OCR: An Empirical Analysis of Parameter-Efficient Adaptation](https://md.research.elamur.ai/free/en/papers/shu-2026-multi-task-learning-trade-offs/) [Workshop on Language Technologies for Historical and Ancient Languages @ LREC](https://md.research.elamur.ai/free/en/venues/lt4hala/)**Yuhan Shu, Huizi Zhou*2026*
22. **[Overview of EvaHan2026: The First International Evaluation of Ancient Chinese OCR and Layout Analysis](https://md.research.elamur.ai/free/en/papers/wang-2026-overview-evahan2026-first-international-evaluation/) [Workshop on Language Technologies for Historical and Ancient Languages @ LREC](https://md.research.elamur.ai/free/en/venues/lt4hala/)**Dongbo Wang, Dongmei Zhu, Jieqiong Li, et al.*2026*
23. **[Reclaiming African Voices: Surveying Indigenous Writing Systems for Inclusive NLP](https://md.research.elamur.ai/free/en/papers/traore-2026-reclaiming-african-voices-surveying-indigenous/) [Resources for African Indigenous Languages (RAIL) @ LREC](https://md.research.elamur.ai/free/en/venues/rail/)**Mamady Traore, Ngoc Tan Le, Fatiha Sadat*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/authors/jinghui-lu/) — Jinghui Lu
