---
title: "Who cites Sam McCandlish — page 4"
description: "3,975 papers of the Elamur Research library cite the works of Sam McCandlish, found in their bibliographies · page 4 of 4 · 301–331; the most cited first."
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---
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# Who cites Sam McCandlish

4,762 citations of their works from 3,975 papers of our library (of the 140,884 whose bibliographies we read) · 331 with a page here, the most cited first · page 4 of 4 · 301–331 · [← Sam McCandlish](https://md.research.elamur.ai/free/en/authors/sam-mccandlish/)

[← prev](https://md.research.elamur.ai/free/en/authors/sam-mccandlish/cited-by/page/3/) [1](https://md.research.elamur.ai/free/en/authors/sam-mccandlish/cited-by/) [2](https://md.research.elamur.ai/free/en/authors/sam-mccandlish/cited-by/page/2/) [3](https://md.research.elamur.ai/free/en/authors/sam-mccandlish/cited-by/page/3/) **4**

301. [**Kuwain 1.5B: An Arabic SLM via Language Injection**Khalil Hennara, Sara Chrouf, Mohamed Motaism Hamed, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/hennara-2025-kuwain-1-5b-arabic-slm/)
302. [**Scaling Laws for Code: A More Data-Hungry Regime**Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/luo-2026-scaling-laws-code-more-data/)
303. [**TafBERTa: Learning Grammatical Rules from Small-Scale Language Acquisition Data in Hebrew**Anita Gelboim, Elior Sulem*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/gelboim-2025-tafberta-learning-grammatical-rules-small/)
304. [**Fine-Tashkeel: Finetuning Byte-Level Models for Accurate Arabic Text Diacritization**Bashar Al-Rfooh, Gheith Abandah, Rami Al-Rfou*2023 · 2 citations*](https://md.research.elamur.ai/free/en/papers/al-rfooh-2023-fine-tashkeel-finetuning-byte-level/)
305. [**AlbanianLLMSafety: A Safety Evaluation Dataset for Large Language Models in Albanian**Wajdi Zaghouani, Kholoud Khalil Aldous, Isra Fejzullaj*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-albanianllmsafety-safety-evaluation-dataset-large/)
306. [**BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning**Ha Thanh Nguyen, Hideyuki Tachibana, Chaoran Liu, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-bis-reasoning-1-0-first/)
307. [**Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety**Qian Shen, Fanghua Cao, Min Yao, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/shen-2026-children-s-english-reading-story/)
308. [**German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies**Jens Rupprecht, Leon Froehling, Claudia Wagner, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/rupprecht-2026-german-general-social-survey-personas/)
309. [**Human-like Fleeting Memory Improves Language Learning but Impairs Reading Time Prediction in Transformer Language Models**Abishek Thamma, Micha Heilbron*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/thamma-2026-human-like-fleeting-memory-improves/)
310. [**In-Distribution Steering: Balancing Control and Coherence in Language Model Generation**Arthur Vogels, Benjamin Wong, Yann Choho, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/vogels-2026-distribution-steering-balancing-control-coherence/)
311. [**Sense and Sensitivity: “Reasoning” Models are More Robust, but can Diverge from Human Consensus in a Legal Interpretation Task**Dawson Petersen, Abhishek Purushothama, Nathan Schneider*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/petersen-2026-sense-sensitivity-reasoning-models-are/)
312. [**What Exactly do Children Receive in Language Acquisition? A Case Study on CHILDES with Automated Detection of Filler-Gap Dependencies**Zhenghao Zhou, William Dai, Maya Viswanathan, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/zhou-2026-what-exactly-do-children-receive/)
313. [**Batch-wise Convergent Pre-training: Step-by-Step Learning Inspired by Child Language Development**Ko Yoshida, Daiki Shiono, Kai Sato, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/yoshida-2025-batch-wise-convergent-pre-training/)
314. [**HAMSA: Hijacking Aligned Compact Models via Stealthy Automation**Alexey Krylov, Iskander Vagizov, Dmitrii Korzh, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/krylov-2025-hamsa-hijacking-aligned-compact-models/)
315. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)
316. [**AgriChain: Visually-Grounded Expert-Verified Reasoning for Interpretable Agricultural Vision–Language Models**Hazza Mahmood, Yongqiang Yu, Rao Anwer*2026*](https://md.research.elamur.ai/free/en/papers/mahmood-2026-agrichain-visually-grounded-expert-verified/)
317. [**AnswerCarefully: Creating a Dataset for LLM Safety in Japanese**Hisami Suzuki, Satoru Katsumata, Takashi Kodama, et al.*2026*](https://md.research.elamur.ai/free/en/papers/suzuki-2026-answercarefully-creating-dataset-llm-safety/)
318. [**Bridging Cognition and Affect: Emotion-Aware Opinion Summarization using LLMs**Arnav Attri, Anuj Attri*2026*](https://md.research.elamur.ai/free/en/papers/attri-2026-bridging-cognition-affect-emotion-aware/)
319. [**Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmark**Yu Wu, Ke Shu, Jonas Fischer, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wu-2026-detecting-latin-historical-books-large/)
320. [**DiPS: Dialogue Policy Selection for High-Stakes Persuasion Agents**Tianyi Zhang, Mousumi Das, Abrar Anwar, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhang-2026-dips-dialogue-policy-selection-high/)
321. [**I Understand How You Feel: Enhancing Deeper Emotional Support Through Multilingual Emotional Validation in Dialogue System**Zi Haur Pang, Yahui Fu, Koji Inoue, et al.*2026*](https://md.research.elamur.ai/free/en/papers/pang-2026-i-understand-how-you-feel/)
322. [**Investigating Memorization in Language Models Trained via Knowledge Distillation**Maarten Mäcking, Michaela Regneri*2026*](https://md.research.elamur.ai/free/en/papers/macking-2026-investigating-memorization-language-models-trained/)
323. [**Judging Instruction Responses in a Low-Resource Language: A Case Study on Basque**David Ponce, Harritxu Gete, Thierry Etchegoyhen, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ponce-2026-judging-instruction-responses-low-resource/)
324. [**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/)
325. [**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/)
326. [**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/)
327. [**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/)
328. [**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/)
329. [**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/)
330. [**Who Generates More Empathetic Responses–-Humans or LLMs? A Comparative Evaluation with Human and LLM Judges**Anuradha Welivita, Fawzia Zeitoun, Pearl Pu*2026*](https://md.research.elamur.ai/free/en/papers/welivita-2026-who-generates-more-empathetic-responses/)
331. [**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/)

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3,644 more citing papers of our library have no page here yet.

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