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title: "Who cites Jared Kaplan — page 11"
description: "15,255 papers of the Elamur Research library cite the works of Jared Kaplan, found in their bibliographies · page 11 of 11 · 1001–1035; the most cited…"
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# Who cites Jared Kaplan

19,286 citations of their works from 15,255 papers of our library (of the 140,884 whose bibliographies we read) · 1,035 with a page here, the most cited first · page 11 of 11 · 1001–1035 · [← Jared Kaplan](https://md.research.elamur.ai/free/en/authors/jared-kaplan/)

[← prev](https://md.research.elamur.ai/free/en/authors/jared-kaplan/cited-by/page/10/) [1](https://md.research.elamur.ai/free/en/authors/jared-kaplan/cited-by/) … [10](https://md.research.elamur.ai/free/en/authors/jared-kaplan/cited-by/page/10/) **11**

1001. [**MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection**David M. Smiley*2026*](https://md.research.elamur.ai/free/en/papers/smiley-2026-miqrabert-regression-based-sentence-bert/)
1002. [**Multi-Agent Debate for Machine Translation: A Case Study on English-Japanese Translation**Zhan Shen, Jason Naradowsky, Xiaotian Wang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/shen-2026-multi-agent-debate-machine-translation/)
1003. [**MUSIA: Multilingual Story Illustration Corpus for Cross-Cultural Alignment and Generation**Krishna Tewari, Supriya Chanda, Nirmit Patil, et al.*2026*](https://md.research.elamur.ai/free/en/papers/tewari-2026-musia-multilingual-story-illustration-corpus/)
1004. [**MzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages**Anri M. Lombard, Simbarashe Mawere, Temi Aina, et al.*2026*](https://md.research.elamur.ai/free/en/papers/lombard-2026-mzansitext-mzansilm-open-corpus-decoder/)
1005. [**NegNLI-BR: A Brazilian Portuguese Benchmark for Negation in Natural Language Inference**Matheus Westhelle, Viviane Moreira*2026*](https://md.research.elamur.ai/free/en/papers/westhelle-2026-negnli-br-brazilian-portuguese-benchmark/)
1006. [**Obscuring Data Contamination Through Translation: Evidence from Arabic Corpora**Chaymaa Abbas, Nour Shamaa, Mariette Awad*2026*](https://md.research.elamur.ai/free/en/papers/abbas-2026-obscuring-data-contamination-through-translation/)
1007. [**On Temperature-Constrained Non-Deterministic Machine Translation: Potential and Evaluation**Weichuan Wang, Mingyang Liu, Chen Ma, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-temperature-constrained-non-deterministic-machine/)
1008. [**Open-access Dataset on Acceptability Ratings of Korean Clausal Constructions by Humans and GPT Models**Gyu-Ho Shin, Soo-Hwan Lee, Chanyoung Lee*2026*](https://md.research.elamur.ai/free/en/papers/shin-2026-open-access-dataset-acceptability-ratings/)
1009. [**Persona-Aware Evaluation of Cognitive Bias in LLMs: From Benchmark to Applied Decision-Making**Katsumasa Yoshikawa, Junya Takayama, Takato Yamazaki*2026*](https://md.research.elamur.ai/free/en/papers/yoshikawa-2026-persona-aware-evaluation-cognitive-bias/)
1010. [**Predict and Fix: A Unified Model for Translation Quality Estimation and Post-Editing**Maciej Modrzejewski, Yash Bhaskar, Chinmay Pateria*2026*](https://md.research.elamur.ai/free/en/papers/modrzejewski-2026-predict-fix-unified-model-translation/)
1011. [**Quality and Appropriateness of Large Text Datasets for Irish NLP**Abigail Walsh, Mark Andrade, Jane Lauren Adkins, et al.*2026*](https://md.research.elamur.ai/free/en/papers/walsh-2026-quality-appropriateness-large-text-datasets/)
1012. [**Questionnaire Meets LLM: A Benchmark and Empirical Study of Structural Skills for Understanding Questions and Responses**Duc-Hai Nguyen, Vijayakumar Nanjappan, Barry O'Sullivan, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-questionnaire-meets-llm-benchmark-empirical/)
1013. [**Reason-to-Learn (R2L): Multi-Agent Knowledge Distillation for Lightweight LLMs in Sentiment Analysis**Le-Huy Tu, Quan Nguyen, Vincent NGUYEN, et al.*2026*](https://md.research.elamur.ai/free/en/papers/tu-2026-reason-learn-r2l-multi-agent/)
1014. [**Recent Advances in Text-attributed Graph Learning via Large Models (大模型驱动的文本属性图学习研究进展)**Yanping Zheng (郑艳萍), Jiarui Ji (季嘉蕊), Yuwei Hu (胡雨韦), et al.*2026*](https://md.research.elamur.ai/free/en/papers/zheng-2026-recent-advances-text/)
1015. [**SalamahBench: Dialect and Category Level Safety Evaluation of Arabic Language Models**Omar Abdelnasser, Fatemah Alharbi, Khaled Khasawneh, et al.*2026*](https://md.research.elamur.ai/free/en/papers/abdelnasser-2026-salamahbench-dialect-category-level-safety/)
1016. [**Sentiment Analysis and Language Models for Kwanyama**Ndapa Nakashole*2026*](https://md.research.elamur.ai/free/en/papers/nakashole-2026-sentiment-analysis-language-models-kwanyama/)
1017. [**Supervised Contrastive Fine-Tuning for Active Few-Shot Learning**Zirui Zhang, Lei Ge, Shengyu Qiao*2026*](https://md.research.elamur.ai/free/en/papers/zhang-2026-supervised-contrastive-fine-tuning-active/)
1018. [**Toxic Subword Pruning for Dialogue Response Generation on Large Language Models**Hongyuan Adam Lu, Wai Lam*2026*](https://md.research.elamur.ai/free/en/papers/lu-2026-toxic-subword-pruning-dialogue-response/)
1019. [**ZIP: Quantifying Which Words Matter in Zero-Shot Instructional Prompts**Nikta Gohari Sadr, Sangmitra Madhusudan, Arash Asgari, et al.*2026*](https://md.research.elamur.ai/free/en/papers/sadr-2026-zip-quantifying-which-words-matter/)
1020. [**“Decode the Law”: Towards Legal Text Simplification with Large Language Models**Mohammed Danish Rabbani, Subhadeep Roy, Sayantan Mitra, et al.*2026*](https://md.research.elamur.ai/free/en/papers/rabbani-2026-decode-law-legal-text-simplification/)
1021. [**ALHD: A Large-Scale and Multigenre Benchmark Dataset for Arabic LLM-Generated Text Detection**Ali Khairallah, Arkaitz Zubiaga*2025*](https://md.research.elamur.ai/free/en/papers/khairallah-2025-alhd-large-scale-multigenre-benchmark/)
1022. [**An Empirical Study of Chinese Entity Linking Based on Large Language Model (基于大语言模型的中文实体链接实证研究)**Zhengfei Xu (徐正斐), Xin Xin (辛欣)*2025*](https://md.research.elamur.ai/free/en/papers/xu-2025-empirical-study-chinese/)
1023. [**Embodied Intelligence Systems Based on Large Models: A Survey (基于大模型的具身智能系统综述)**Wensheng Wang (王文晟), Ning Tan (谭宁), Kai Huang (黄凯), et al.*2025*](https://md.research.elamur.ai/free/en/papers/wang-2025-embodied-intelligence-systems/)
1024. [**NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew**Shaltiel Shmidman, Avi Shmidman, Moshe Koppel*2025*](https://md.research.elamur.ai/free/en/papers/shmidman-2025-neodictabert-pushing-frontier-bert-models/)
1025. [**Poem Meter Classification of Recited Arabic Poetry: Integrating High-Resource Systems for a Low-Resource Task**Maged S. Al-Shaibani, Zaid Alyafeai, Irfan Ahmad*2025*](https://md.research.elamur.ai/free/en/papers/al-shaibani-2025-poem-meter-classification-recited-arabic/)
1026. [**Survey of Tool Use in Large Language Models (大语言模型的工具使用综述)**Yining Zheng (郑逸宁), Zhen Yu (余镇), Bufan Li (李不凡), et al.*2025*](https://md.research.elamur.ai/free/en/papers/zheng-2025-tool-use-large/)
1027. [**Survey on Application of Retrieval-augmented Generation in Software Engineering (检索增强生成在软件工程中的应用综述)**Quanjun Zhang (张犬俊), Yang Xie (谢杨), Chunrong Fang (房春荣), et al.*2025*](https://md.research.elamur.ai/free/en/papers/zhang-2025-application-retrieval-augmented/)
1028. [**Survey on Code Generation with LLM-based Agents (基于大语言模型智能体的代码生成综述)**Yihong Dong (董益宏), Xue Jiang (姜雪), Jiaru Qian (钱家如), et al.*2025*](https://md.research.elamur.ai/free/en/papers/dong-2025-code-generation-llm/)
1029. [**Survey on Key Technologies for Large Language Model Pre-training Systems (大语言模型预训练系统关键技术综述)**Yanjie Gao (高彦杰), Yueguo Chen (陈跃国)*2025*](https://md.research.elamur.ai/free/en/papers/gao-2025-key-technologies-large/)
1030. [**Can generative AI recognise textual entailment? (生成 AI は含意関係認識ができるのか)**荒沢康平, 狩野芳伸*2024*](https://md.research.elamur.ai/free/en/papers/arasawa-2024-can-generative-ai-recognise/)
1031. [**Complexity factors of language models in reading comprehension: a controlled experiment (Étude des facteurs de complexité des modèles de langage dans une tâche de compréhension de lecture à l'aide d'une expérience contrôlée sémantiquement)**Elie Antoine, Frederic Bechet, Géraldine Damnati, et al.*2024*](https://md.research.elamur.ai/free/en/papers/antoine-2024-etude-des-facteurs-de-complexite/)
1032. [**Human Temporal Inferences Go Beyond Aspectual Class**Katarzyna Pruś, Mark Steedman, Adam Lopez*2024*](https://md.research.elamur.ai/free/en/papers/prus-2024-human-temporal-inferences-go-beyond/)
1033. [**On the importance of Data Scale in Pretraining Arabic Language Models**Abbas Ghaddar, Philippe Langlais, Mehdi Rezagholizadeh, et al.*2024*](https://md.research.elamur.ai/free/en/papers/ghaddar-2024-importance-data-scale-pretraining-arabic/)
1034. [**Robustness of GPT Large Language Models on Natural Language Processing Tasks (GPT系列大语言模型在自然语言处理任务中的鲁棒性)**Xuanting Chen (陈炫婷), Junjie Ye (叶俊杰), Can Zu (祖璨), et al.*2024*](https://md.research.elamur.ai/free/en/papers/chen-2024-robustness-gpt-large/)
1035. [**Questions After ChatGPT: Opportunities and Challenges of Very Large Pre-trained Models (问答ChatGPT之后: 超大预训练模型的机遇和挑战)**Jingwei Lu (卢经纬), Chao Guo (郭超), Xingyuan Dai (戴星原), et al.*2023*](https://md.research.elamur.ai/free/en/papers/lu-2023-questions-after-chatgpt/)

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