---
title: "Who cites Tom Henighan — page 4"
description: "3,704 papers of the Elamur Research library cite the works of Tom Henighan, found in their bibliographies · page 4 of 4 · 301–321; the most cited first."
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# Who cites Tom Henighan

4,429 citations of their works from 3,704 papers of our library (of the 140,884 whose bibliographies we read) · 321 with a page here, the most cited first · page 4 of 4 · 301–321 · [← Tom Henighan](https://md.research.elamur.ai/free/en/authors/tom-henighan/)

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

301. [**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/)
302. [**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/)
303. [**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/)
304. [**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/)
305. [**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/)
306. [**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/)
307. [**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/)
308. [**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/)
309. [**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/)
310. [**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/)
311. [**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/)
312. [**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/)
313. [**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/)
314. [**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/)
315. [**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/)
316. [**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/)
317. [**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/)
318. [**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/)
319. [**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/)
320. [**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/)
321. [**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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3,383 more citing papers of our library have no page here yet.

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