---
title: "Who cites Roger Grosse — page 3 — 902 papers of our library"
description: "902 papers of the Elamur Research library cite the works of Roger Grosse, found in their bibliographies · page 3 of 3 · 201–254; the most cited first."
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# Who cites Roger Grosse

964 citations of their works from 902 papers of our library (of the 149,439 whose bibliographies we read) · 254 with a page here, the most cited first · page 3 of 3 · 201–254 · [← Roger Grosse](https://md.research.elamur.ai/free/en/authors/roger-grosse/)

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

201. **[In-Distribution Steering: Balancing Control and Coherence in Language Model Generation](https://md.research.elamur.ai/free/en/papers/vogels-2026-distribution-steering-balancing-control-coherence/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Arthur Vogels, Benjamin Wong, Yann Choho, et al.*2026 · 1 citation*
202. **[Mechanistic Interpretability Should Prioritize Feature Consistency in Sparse Autoencoders](https://md.research.elamur.ai/free/en/papers/song-2026-mechanistic-interpretability-should-prioritize-feature/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiangchen Song, Aashiq Muhamed, Yujia Zheng, et al.*2026 · 1 citation*
203. **[PARASITE: Conditional System Prompt Poisoning to Hijack LLMs](https://md.research.elamur.ai/free/en/papers/pham-2026-parasite-conditional-system-prompt-poisoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Viet Pham, Thai Le*2026 · 1 citation*
204. [**PseudoBench: Measuring How Agentic Auto-Research Fuels Pseudoscience arXiv:2606.18060**Xinyang Liao, Lingyu Li, Huacan Liu, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/liao-2026-pseudobench-measuring-agentic-auto/)
205. **[Sounding vs. Being an Expert: Disentangling Authority, Register and Cultural Impact in Sycophantic LLMs](https://md.research.elamur.ai/free/en/papers/maraia-2026-sounding-vs-being-expert-disentangling/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Gabriele Maraia, Fabio Massimo Zanzotto, Leonardo Ranaldi*2026 · 1 citation*
206. **[SycoBench-600: Measuring Sycophancy and Correction Selectivity in LLM Assistants](https://md.research.elamur.ai/free/en/papers/sinha-2026-sycobench-600-measuring-sycophancy-correction/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Debu Sinha*2026 · 1 citation*
207. **[Test of Time: Rethinking Temporal Signal of Benchmark Contamination](https://md.research.elamur.ai/free/en/papers/zhang-2026-test-time-rethinking-temporal-signal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Terry Jingchen Zhang, Gopal Dev, Ning Wang, et al.*2026 · 1 citation*
208. **[The Pluralistic Moral Gap: Understanding Moral Judgment and Value Differences between Humans and Large Language Models](https://md.research.elamur.ai/free/en/papers/russo-2026-pluralistic-moral-gap-understanding-moral/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Giuseppe Russo, Debora Nozza, Paul Röttger, et al.*2026 · 1 citation*
209. **[Trajectory Signatures of Deception in Large Language Models](https://md.research.elamur.ai/free/en/papers/mothukuri-2026-trajectory-signatures-deception-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Viraaji Mothukuri, Reza M. Parizi*2026 · 1 citation*
210. **[Understanding and Mitigating Political Stance Cross-topic Generalization in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-understanding-mitigating-political-stance-cross/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiayi Zhang, Shu Yang, Junchao Wu, et al.*2026 · 1 citation*
211. **[What Exactly do Children Receive in Language Acquisition? A Case Study on CHILDES with Automated Detection of Filler-Gap Dependencies](https://md.research.elamur.ai/free/en/papers/zhou-2026-what-exactly-do-children-receive/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Zhenghao Zhou, William Dai, Maya Viswanathan, et al.*2026 · 1 citation*
212. [**TracrBench: Generating Interpretability Testbeds with Large Language Models arXiv:2409.13714**Hannes Thurnherr, Jérémy Scheurer*2024 · 1 citation*](https://md.research.elamur.ai/free/en/papers/thurnherr-2024-tracrbench-generating-interpretability-testbeds/)
213. **[AdaptiveK: Complexity-Driven Sparse Autoencoders for Interpretable Language Model Representations](https://md.research.elamur.ai/free/en/papers/yao-2026-adaptivek-complexity-driven-sparse-autoencoders/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yifei Yao, Hanrong Zhang, Mengnan Du*2026*
214. **[At Your Own PACE: A Causal Framework for Evaluating EQ in LLMs](https://md.research.elamur.ai/free/en/papers/lyu-2026-your-own-pace-causal-framework/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lei Lyu, Shengling Wang, Ke Chao, et al.*2026*
215. **[Beyond Marginal Distributions: A Framework to Evaluate the Representativeness of Demographic-Aligned LLMs](https://md.research.elamur.ai/free/en/papers/williams-2026-beyond-marginal-distributions-framework-evaluate/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tristan Williams, Franziska Weeber, Sebastian Padó, et al.*2026*
216. **[Beyond the Safety Tax: Mitigating Unsafe Text-to-Image Generation via External Safety Rectification](https://md.research.elamur.ai/free/en/papers/meng-2026-beyond-safety-tax-mitigating-unsafe/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiangtao Meng, Yingkai Dong, Ning Yu, et al.*2026*
217. **[Bridging Cognition and Affect: Emotion-Aware Opinion Summarization using LLMs](https://md.research.elamur.ai/free/en/papers/attri-2026-bridging-cognition-affect-emotion-aware/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Arnav Attri, Anuj Attri*2026*
218. **[Compact Example-Based Explanations for Language Models](https://md.research.elamur.ai/free/en/papers/schoenegger-2026-compact-example-based-explanations-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Loris Schoenegger, Benjamin Roth*2026*
219. [**Computational models of pragmatic reasoning with flexible generation of meaning and expression alternatives arXiv:2607.18443**Polina Tsvilodub, Fausto Carcassi, Michael Franke*2026*](https://md.research.elamur.ai/free/en/papers/tsvilodub-2026-computational-models-pragmatic-reasoning-flexible/)
220. **[CoopValue: Revealing LLM Value Preferences Through Multi-Agent Cooperation](https://md.research.elamur.ai/free/en/papers/tang-2026-coopvalue-revealing-llm-value-preferences/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zee Hen Tang, Mi-Yen Yeh*2026*
221. **[DART: Mitigating Harm Drift in Difference-Aware LLMs via Distill-Audit-Repair Training](https://md.research.elamur.ai/free/en/papers/pan-2026-dart-mitigating-harm-drift-difference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ziwen Pan, Zihan Liang, Jad Kabbara, et al.*2026*
222. **[Data Efficient RLVR via Off-Policy Influence Guidance](https://md.research.elamur.ai/free/en/papers/zhu-2026-data-efficient-rlvr-off-policy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Erle Zhu, Dazhi Jiang, Yuan Wang, et al.*2026*
223. **[DialDefer: A Framework for Detecting and Mitigating LLM Dialogic Deference](https://md.research.elamur.ai/free/en/papers/rabbani-2026-dialdefer-framework-detecting-mitigating-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Parisa Rabbani, Priyam Sahoo, Ruben Mathew, et al.*2026*
224. **[Evaluating Language Model Pluralism through In-the-wild Crowd Discussions](https://md.research.elamur.ai/free/en/papers/mundada-2026-evaluating-language-model-pluralism-through/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gagan Mundada, Rohan Surana, Nandhini Swaminathan, et al.*2026*
225. **[Evaluating the Impact of SAE-based Language Steering on LLM Performance](https://md.research.elamur.ai/free/en/papers/zwirner-2026-evaluating-impact-sae-based-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sebastian Zwirner, Wentao Hu, Koshiro Aoki, et al.*2026*
226. **[Faithfulness vs. Safety: Evaluating LLM Behavior Under Counterfactual Medical Evidence](https://md.research.elamur.ai/free/en/papers/mo-2026-faithfulness-vs-safety-evaluating-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaijie Mo, Siddhartha Venkatayogi, Chantal Shaib, et al.*2026*
227. **[Feeling Right vs. Being Right: How AI Sycophancy Affects Value-Laden Deliberation](https://md.research.elamur.ai/free/en/papers/ryu-2026-feeling-right-vs-being-right/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jeongwoo Ryu, Soomin Kim, Jinsu Eun, et al.*2026*
228. **[From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models](https://md.research.elamur.ai/free/en/papers/shi-2026-insight-action-novel-framework-interpretability/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ling Shi, Xinwei Wu, Xiaohu Zhao, et al.*2026*
229. **[From Latents to Labels: Zero-Shot Named Entity Recognition using Sparse Autoencoder Features](https://md.research.elamur.ai/free/en/papers/vuth-2026-latents-labels-zero-shot-named/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Nakanyseth Vuth, Gilles Sérasset, Didier Schwab*2026*
230. **[How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities](https://md.research.elamur.ai/free/en/papers/xu-2026-how-controllable-are-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziwen Xu, Kewei Xu, Haoming Xu, et al.*2026*
231. **[How Do LLMs Generate Contrastive Sentiments? A Mechanistic Perspective](https://md.research.elamur.ai/free/en/papers/nguyen-2026-how-do-llms-generate-contrastive/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Van Bach Nguyen, Jörg Schlötterer, Christin Seifert*2026*
232. **[Interpretable Safety Alignment via SAE-Constructed Low-Rank Subspace Adaptation](https://md.research.elamur.ai/free/en/papers/wang-2026-interpretable-safety-alignment-sae-constructed/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dianyun Wang, Qingsen Ma, Yuhu Shang, et al.*2026*
233. **[Interpretable Semantic Gradients in SSD: A PCA Sweep Approach and a Case Study on AI Discourse](https://md.research.elamur.ai/free/en/papers/plisiecki-2026-interpretable-semantic-gradients-ssd-pca/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hubert Plisiecki, Maria Leniarska, Jan Piotrowski, et al.*2026*
234. **[Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation](https://md.research.elamur.ai/free/en/papers/tang-2026-knowledge-is-not-enough-injecting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pingzhi Tang, Yiding Wang, Muhan Zhang*2026*
235. **[Knowledge Vector of Logical Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-knowledge-vector-logical-reasoning-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zixuan Wang, Yuanyuan Lei*2026*
236. **[Lexical Familiarity Predicts Processing Depth for Nonliteral Language in Large Language Models](https://md.research.elamur.ai/free/en/papers/yeh-2026-lexical-familiarity-predicts-processing-depth/) [Workshop on Trustworthy NLP](https://md.research.elamur.ai/free/en/venues/trustnlp/)**Lang-Ching Yeh, Yu-Chieh Wang, Shu-Kai Hsieh*2026*
237. **[LLM-VA: Resolving the Jailbreak-Overrefusal Trade-off via Vector Alignment](https://md.research.elamur.ai/free/en/papers/zhang-2026-llm-va-resolving-jailbreak-overrefusal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haonan Zhang, Dongxia Wang, Yi Liu, et al.*2026*
238. **[Mapping the Circumplex of Affect: Geometric Analysis of Emotion Representations via Hyperspherical Contrastive Learning](https://md.research.elamur.ai/free/en/papers/yamauchi-2026-mapping-circumplex-affect-geometric-analysis/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yusuke Yamauchi, Akiko Aizawa*2026*
239. **[MERIT Feedback Elicits Better Bargaining in LLM Negotiators](https://md.research.elamur.ai/free/en/papers/oh-2026-merit-feedback-elicits-better-bargaining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jihwan Oh, Murad Aghazada, Yooju Shin, et al.*2026*
240. **[Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders](https://md.research.elamur.ai/free/en/papers/zheng-2026-model-directions-not-words-mechanistic/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Carolina Zheng, Nicolas Beltran-Velez, Sweta Karlekar, et al.*2026*
241. **[Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not Stable](https://md.research.elamur.ai/free/en/papers/tang-2026-probing-plasticity-correlation-llm-value/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhenheng Tang, Qihua Pan, Jingya Shen, et al.*2026*
242. **[Quantifying and Mitigating Socially Desirable Responding in LLMs: A Desirability-Matched Graded Forced-Choice Psychometric Study](https://md.research.elamur.ai/free/en/papers/okada-2026-quantifying-mitigating-socially-desirable-responding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kensuke Okada, Yui Furukawa, Kyosuke Bunji*2026*
243. **[Representation-Guided Parameter-Efficient LLM Unlearning](https://md.research.elamur.ai/free/en/papers/xiao-2026-representation-guided-parameter-efficient-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zeguan Xiao, Lang Mo, Yun Chen, et al.*2026*
244. **[Rewriting History: A Recipe for Interventional Analyses to Study Data Effects on Model Behavior](https://md.research.elamur.ai/free/en/papers/nadkarni-2026-rewriting-history-recipe-interventional-analyses/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Rahul Nadkarni, Yanai Elazar, Hila Gonen, et al.*2026*
245. **[Selective Span-Level Unlearning for Large Language Models](https://md.research.elamur.ai/free/en/papers/yoon-2026-selective-span-level-unlearning-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chaewon Yoon, Dongjun Kim, Hyun-Je Song*2026*
246. **[Teaching Values to Machines: Simulating Human-Like Behavior in LLMs](https://md.research.elamur.ai/free/en/papers/yehudai-2026-teaching-values-machines-simulating-human/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Asaf Yehudai, Naama Rozen, Ariel Gera*2026*
247. **[The Impact of Off-Policy Training Data on Probe Generalisation](https://md.research.elamur.ai/free/en/papers/skapars-2026-impact-off-policy-training-data/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Adrians Skapars, Nathalie Maria Kirch, Samuel Dower, et al.*2026*
248. **[TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-training](https://md.research.elamur.ai/free/en/papers/wang-2026-tikmix-efficient-semi-dynamic-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifan Wang, Binbinliu, Fengze Liu, et al.*2026*
249. **[Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language Models](https://md.research.elamur.ai/free/en/papers/shah-2026-too-nice-tell-truth-quantifying/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Arya Shah, Deepali Mishra, Chaklam Silpasuwanchai*2026*
250. **[Tracing Relational Knowledge Recall in Large Language Models](https://md.research.elamur.ai/free/en/papers/popovic-2026-tracing-relational-knowledge-recall-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nicholas Popovič, Michael Färber*2026*
251. **[Understanding Emergent Misalignment via Feature Superposition Geometry](https://md.research.elamur.ai/free/en/papers/minegishi-2026-understanding-emergent-misalignment-feature-superposition/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gouki Minegishi, Hiroki Furuta, Takeshi Kojima, et al.*2026*
252. **[Unveiling Intrinsic Dimension of Texts: from Academic Abstract to Creative Story](https://md.research.elamur.ai/free/en/papers/vladislav-2026-unveiling-intrinsic-dimension-texts-academic/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Pedashenko Vladislav, Laida Kushnareva, Yana Khassan Nibal, et al.*2026*
253. **[User-Assistant Bias in LLMs](https://md.research.elamur.ai/free/en/papers/pan-2026-user-assistant-bias-llms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xu Pan, Jingxuan Fan, Zidi Xiong, et al.*2026*
254. **[When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs](https://md.research.elamur.ai/free/en/papers/sun-2026-when-personalization-misleads-understanding-mitigating/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhongxiang Sun, Yi Zhan, Chenglei Shen, et al.*2026*

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