---
title: "Who cites Miles Turpin — 438 papers of our library"
description: "438 papers of the Elamur Research library cite the works of Miles Turpin, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/miles-turpin/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/miles-turpin/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Miles Turpin](https://md.research.elamur.ai/free/en/authors/miles-turpin/) / cited by

# Who cites Miles Turpin

444 citations of their works from 438 papers of our library (of the 149,439 whose bibliographies we read) · 111 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Miles Turpin](https://md.research.elamur.ai/free/en/authors/miles-turpin/)

**1** [2](https://md.research.elamur.ai/free/en/authors/miles-turpin/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/authors/miles-turpin/cited-by/page/2/)

1. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
2. [**OpenAI o1 System Card arXiv:2412.16720**OpenAI, Aaron Jaech, Adam Kalai, et al.*2024 · 1,110 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-openai-o1-system-card/)
3. **[Explainability for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 790 citations*
4. [**Representation Engineering: A Top-Down Approach to AI Transparency arXiv:2310.01405**Andy Zou, Long Phan, Sarah Chen, et al.*2023 · 397 citations*](https://md.research.elamur.ai/free/en/papers/zou-2023-representation-engineering-top-down-approach/)
5. [**Towards Understanding Sycophancy in Language Models arXiv:2310.13548**Mrinank Sharma, Meg Tong, Tomasz Korbak, et al.*2023 · 384 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2023-towards-understanding-sycophancy-language-models/)
6. **[Faithful Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/lyu-2023-faithful-chain-thought-reasoning/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Qing Lyu, Shreya Havaldar, Adam Stein, et al.*2023 · 353 citations*
7. [**Hallucination is Inevitable: An Innate Limitation of Large Language Models arXiv:2401.11817**Ziwei Xu, Sanjay Jain, Mohan Kankanhalli*2024 · 312 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-hallucination-inevitable-innate-limitation-large/)
8. [**Measuring Faithfulness in Chain-of-Thought Reasoning arXiv:2307.13702**Tamera Lanham, Anna Chen, Ansh Radhakrishnan, et al.*2023 · 262 citations*](https://md.research.elamur.ai/free/en/papers/lanham-2023-measuring-faithfulness-chain-thought-reasoning/)
9. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
10. **[On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://md.research.elamur.ai/free/en/papers/shaikh-2023-second-thought-let-s-not/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Omar Shaikh, Hongxin Zhang, William Held, et al.*2023 · 192 citations*
11. **[Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 182 citations*
12. [**AI Alignment: A Comprehensive Survey arXiv:2310.19852**Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.*2023 · 173 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-ai-alignment-comprehensive-survey/)
13. **[WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs](https://md.research.elamur.ai/free/en/papers/han-2024-wildguard-open-one-stop-moderation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Seungju Han, Kavel Rao, Allyson Ettinger, et al.*2024 · 165 citations*
14. **[LLaVA-CoT: Let Vision Language Models Reason Step-by-Step](https://md.research.elamur.ai/free/en/papers/xu-2024-llava-cot-let-vision-language/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Guowei Xu, Peng Jin, Ziang Wu, et al.*2024 · 138 citations*
15. [**Simple synthetic data reduces sycophancy in large language models arXiv:2308.03958**Jerry Wei, Da Huang, Yifeng Lu, et al.*2023 · 134 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-simple-synthetic-data-reduces-sycophancy/)
16. **[Prompting is not a substitute for probability measurements in large language models](https://md.research.elamur.ai/free/en/papers/hu-2023-prompting-is-not-substitute-probability/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jennifer Hu, Roger Levy*2023 · 133 citations*
17. [**Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations arXiv:2310.06387**Zeming Wei, Yifei Wang, Ang Li, et al.*2023 · 130 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-jailbreak-guard-aligned-language-models/)
18. **[Sycophancy in Large Language Models: Causes and Mitigations](https://md.research.elamur.ai/free/en/papers/malmqvist-2024-sycophancy-large-language-models-causes/) [Lecture notes in networks and systems](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-networks-and-systems/)**Lars Malmqvist*2024 · 123 citations*
19. [**The Alignment Problem from a Deep Learning Perspective arXiv:2209.00626**Richard Ngo, Lawrence Chan, Soren Mindermann*2022 · 121 citations*](https://md.research.elamur.ai/free/en/papers/ngo-2022-alignment-problem-deep-learning-perspective/)
20. **[Automated evaluation of written discourse coherence using GPT-4](https://md.research.elamur.ai/free/en/papers/naismith-2023-automated-evaluation-written-discourse-coherence/) [Workshop on Innovative Use of NLP for Building Educational Applications (BEA)](https://md.research.elamur.ai/free/en/venues/bea/)**Ben Naismith, Phoebe Mulcaire, Jill Burstein*2023 · 120 citations*
21. [**Can LLM-Generated Misinformation Be Detected? arXiv:2309.13788**Canyu Chen, Kai Shu*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-llm-generated-misinformation-detected/)
22. **[Leveraging GPT-4 for Automatic Translation Post-Editing](https://md.research.elamur.ai/free/en/papers/raunak-2023-leveraging-gpt-4-automatic-translation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Vikas Raunak, Amr Sharaf, Yiren Wang, et al.*2023 · 108 citations*
23. [**Reasoning Models Don't Always Say What They Think arXiv:2505.05410**Yanda Chen, Joe Benton, Ansh Radhakrishnan, et al.*2025 · 106 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-reasoning-models-don-t-always/)
24. **[Multi-Step Reasoning with Large Language Models, a Survey](https://md.research.elamur.ai/free/en/papers/plaat-2024-multi-step-reasoning-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Aske Plaat, Annie Wong, Suzan Verberne, et al.*2024 · 106 citations*
25. **[WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2024-wildteaming-scale-wild-jailbreaks-adversarially/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Liwei Jiang, Kavel Rao, Seungju Han, et al.*2024 · 92 citations*
26. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
27. [**Rethinking Interpretability in the Era of Large Language Models arXiv:2402.01761**Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-rethinking-interpretability-era-large-language/)
28. [**ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing arXiv:2306.00622**Ryan Liu, Nihar B. Shah*2023 · 84 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-reviewergpt-exploratory-study-using/)
29. **[Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/paul-2024-making-reasoning-matter-measuring-improving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Debjit Paul, Robert West, Antoine Bosselut, et al.*2024 · 79 citations*
30. **[KMMLU: Measuring Massive Multitask Language Understanding in Korean](https://md.research.elamur.ai/free/en/papers/son-2025-kmmlu-measuring-massive-multitask-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Guijin Son, Hanwool Lee, Sungdong Kim, et al.*2025 · 76 citations*
31. [**OR-Bench: An Over-Refusal Benchmark for Large Language Models arXiv:2405.20947**Justin Cui, Wei-Lin Chiang, Ion Stoica, et al.*2024 · 76 citations*](https://md.research.elamur.ai/free/en/papers/cui-2024-bench-over-refusal-benchmark-large/)
32. **[ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness](https://md.research.elamur.ai/free/en/papers/prasad-2023-receval-evaluating-reasoning-chains-correctness/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Archiki Prasad, Swarnadeep Saha, Xiang Zhou, et al.*2023 · 73 citations*
33. [**A Primer on the Inner Workings of Transformer-based Language Models arXiv:2405.00208**Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/ferrando-2024-primer-inner-workings-transformer-based/)
34. **[Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge](https://md.research.elamur.ai/free/en/papers/shi-2025-judging-judges-systematic-study-position/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Lin Shi, Chiyu Ma, Wenhua Liang, et al.*2025 · 71 citations*
35. **[Aligner: Efficient Alignment by Learning to Correct](https://md.research.elamur.ai/free/en/papers/ji-2024-aligner-efficient-alignment-learning-correct/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jiaming Ji, Boyuan Chen, Hantao Lou, et al.*2024 · 69 citations*
36. [**Phi-4-reasoning Technical Report arXiv:2504.21318**Marah Abdin, Sahaj Agarwal, Ahmed Awadallah, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2025-phi-4-reasoning-technical-report/)
37. **[A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners](https://md.research.elamur.ai/free/en/papers/jiang-2024-peek-into-token-bias-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, et al.*2024 · 64 citations*
38. [**Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves arXiv:2311.04205**Yihe Deng, Weitong Zhang, Zixiang Chen, et al.*2023 · 62 citations*](https://md.research.elamur.ai/free/en/papers/deng-2023-rephrase-respond-let-large-language/)
39. **[The Art of Saying No: Contextual Noncompliance in Language Models](https://md.research.elamur.ai/free/en/papers/brahman-2024-art-saying-no-contextual-noncompliance/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Faeze Brahman, Sachin Kumar, Vidhisha Balachandran, et al.*2024 · 56 citations*
40. [**A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond arXiv:2503.21614**Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/qu-2025-survey-efficient-reasoning-large-reasoning/)
41. **[Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?](https://md.research.elamur.ai/free/en/papers/balepur-2024-artifacts-or-abduction-how-do/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nishant Balepur, Abhilasha Ravichander, Rachel Rudinger*2024 · 52 citations*
42. [**Contrastive Chain-of-Thought Prompting arXiv:2311.09277**Yew Ken Chia, Guizhen Chen, Luu Anh Tuan, et al.*2023 · 45 citations*](https://md.research.elamur.ai/free/en/papers/chia-2023-contrastive-chain-thought-prompting/)
43. **[Improving Chain-of-Thought Reasoning via Quasi-Symbolic Abstractions](https://md.research.elamur.ai/free/en/papers/ranaldi-2025-improving-chain-thought-reasoning-quasi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Leonardo Ranaldi, Marco Valentino, Andre Freitas*2025 · 42 citations*
44. [**Observational Scaling Laws and the Predictability of Language Model Performance arXiv:2405.10938**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 40 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
45. [**Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions arXiv:2307.13339**Skyler Wu, Eric Meng Shen, Charumathi Badrinath, et al.*2023 · 30 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-analyzing-chain-thought-prompting-large/)
46. **[Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above](https://md.research.elamur.ai/free/en/papers/balepur-2025-which-these-best-describes-multiple/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nishant Balepur, Rachel Rudinger, Jordan Boyd-Graber*2025 · 28 citations*
47. **[Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models](https://md.research.elamur.ai/free/en/papers/zhifei-2025-audio-reasoner-improving-reasoning-capability/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xie Zhifei, Mingbao Lin, Zihang Liu, et al.*2025 · 25 citations*
48. **[The State of Multilingual LLM Safety Research: From Measuring The Language Gap To Mitigating It](https://md.research.elamur.ai/free/en/papers/yong-2025-state-multilingual-llm-safety-research/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zheng Xin Yong, Beyza Ermis, Marzieh Fadaee, et al.*2025 · 25 citations*
49. [**Language Model Alignment in Multilingual Trolley Problems arXiv:2407.02273**Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/jin-2024-language-model-alignment-multilingual-trolley/)
50. **[How Likely Do LLMs with CoT Mimic Human Reasoning?](https://md.research.elamur.ai/free/en/papers/bao-2025-how-likely-do-llms-cot/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Guangsheng Bao, Hongbo Zhang, Cunxiang Wang, et al.*2025 · 22 citations*
51. **[Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS](https://md.research.elamur.ai/free/en/papers/xu-2026-self-correcting-rag-enhancing-faithfulness/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shijia Xu, Zhou Wu, Xiaolong Jia, et al.*2026 · 15 citations*
52. **[Measuring Chain of Thought Faithfulness by Unlearning Reasoning Steps](https://md.research.elamur.ai/free/en/papers/tutek-2025-measuring-chain-thought-faithfulness-unlearning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Martin Tutek, Fateme Hashemi Chaleshtori, Ana Marasovic, et al.*2025 · 15 citations*
53. **[Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms](https://md.research.elamur.ai/free/en/papers/wang-2025-beyond-prompt-engineering-robust-behavior/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengru Wang, Ziwen Xu, Shengyu Mao, et al.*2025 · 14 citations*
54. **[CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/leang-2025-comat-chain-mathematically-annotated-thought/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Joshua Ong Jun Leang, Aryo Pradipta Gema, Shay B. Cohen*2025 · 14 citations*
55. [**Psychometric Alignment: Capturing Human Knowledge Distributions via Language Models arXiv:2407.15645**Joy He-Yueya, Wanjing Anya Ma, Kanishk Gandhi, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/he-yueya-2024-psychometric-alignment-capturing-human-knowledge/)
56. **[Stepwise Reasoning Disruption Attack of LLMs](https://md.research.elamur.ai/free/en/papers/peng-2025-stepwise-reasoning-disruption-attack-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingyu Peng, Maolin Wang, Xiangyu Zhao, et al.*2025 · 10 citations*
57. **[FaithLM: Towards Faithful Explanations for Large Language Models](https://md.research.elamur.ai/free/en/papers/chuang-2026-faithlm-faithful-explanations-large-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, et al.*2026 · 9 citations*
58. **[Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study](https://md.research.elamur.ai/free/en/papers/song-2025-can-many-shot-context-learning/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Mingyang Song, Mao Zheng, Xuan Luo*2025 · 9 citations*
59. **[Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning](https://md.research.elamur.ai/free/en/papers/bi-2025-enhancing-reasoning-capabilities-small-language/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Jing Bi, Yuting Wu, Weiwei Xing, et al.*2025 · 8 citations*
60. **[A Comprehensive Evaluation of Multilingual Chain-of-Thought Reasoning: Performance, Consistency, and Faithfulness Across Languages](https://md.research.elamur.ai/free/en/papers/zhao-2026-comprehensive-evaluation-multilingual-chain-thought/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Raoyuan Zhao, Yihong Liu, Hinrich Schuetze, et al.*2026 · 7 citations*
61. **[FACT-E: Causality-Inspired Evaluation for Trustworthy Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/sun-2026-fact-e-causality-inspired-evaluation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxi Sun, Aoqi Zuo, Haotian Xie, et al.*2026 · 7 citations*
62. **[CODERL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment](https://md.research.elamur.ai/free/en/papers/jiang-2026-coderl-improving-code-generation-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xue Jiang, Yihong Dong, Mengyang Liu, et al.*2026 · 5 citations*
63. **[False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize](https://md.research.elamur.ai/free/en/papers/wang-2026-false-sense-security-why-probing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cheng Wang, Zeming Wei, Qin Liu, et al.*2026 · 5 citations*
64. **[Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint Verbalization](https://md.research.elamur.ai/free/en/papers/zaman-2026-is-chain-thought-really-not/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kerem Zaman, Shashank Srivastava*2026 · 5 citations*
65. **[Analysing Chain of Thought Dynamics: Active Guidance or Unfaithful Post-hoc Rationalisation?](https://md.research.elamur.ai/free/en/papers/lim-2025-analysing-chain-thought-dynamics-active/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Samuel Lewis-Lim, Xingwei Tan, Zhixue Zhao, et al.*2025 · 5 citations*
66. **[HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Router](https://md.research.elamur.ai/free/en/papers/mei-2026-hiddenguard-fine-grained-safe-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lingrui Mei, Shenghua Liu, Yiwei Wang, et al.*2026 · 4 citations*
67. **[Accurate Legal Reasoning at Scale: Neuro-Symbolic Offloading and Structural Auditability for Robust Legal Adjudication](https://md.research.elamur.ai/free/en/papers/sojka-2026-accurate-legal-reasoning-scale-neuro/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Stanisław Sójka, Witold Kowalczyk*2026 · 2 citations*
68. **[Argument-Based Consistency in Toxicity Explanations of LLMs](https://md.research.elamur.ai/free/en/papers/mothilal-2026-argument-based-consistency-toxicity-explanations/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ramaravind Kommiya Mothilal, Joanna Roy, Syed Ishtiaque Ahmed, et al.*2026 · 2 citations*
69. **[AutoMonitor-Bench: Evaluating the Reliability of LLM-Based Misbehavior Monitor](https://md.research.elamur.ai/free/en/papers/yang-2026-automonitor-bench-evaluating-reliability-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shu Yang, Jingyu Hu, Tong Li, et al.*2026 · 2 citations*
70. **[C2-Faith: Benchmarking LLM Judges for Causal and Coverage Faithfulness in Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/mittal-2026-c2-faith-benchmarking-llm-judges/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Avni Mittal, Rauno Arike*2026 · 2 citations*
71. **[From Belief Entrenchment to Robust Reasoning in LLM Agents](https://md.research.elamur.ai/free/en/papers/oh-2026-belief-entrenchment-robust-reasoning-llm/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jihwan Oh, Minchan Jeong, Jongwoo Ko, et al.*2026 · 2 citations*
72. **[LLM Reasoning as Trajectories: Step-Specific Representation Geometry and Correctness Signals](https://md.research.elamur.ai/free/en/papers/sun-2026-llm-reasoning-as-trajectories-step/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lihao Sun, Hang Dong, Bo Qiao, et al.*2026 · 2 citations*
73. **[Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation](https://md.research.elamur.ai/free/en/papers/zhou-2026-table-question-answering-era-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wei Zhou, Bolei Ma, Annemarie Friedrich, et al.*2026 · 2 citations*
74. **[A Comprehensive Evaluation of Chain-of-Thought Faithfulness in Persian Classification Tasks](https://md.research.elamur.ai/free/en/papers/yazdani-2026-comprehensive-evaluation-chain-thought-faithfulness/) [Workshop on Language Models for Low-Resource Languages](https://md.research.elamur.ai/free/en/venues/loreslm/)**Shakib Yazdani, Cristina España-Bonet, Eleftherios Avramidis, et al.*2026 · 1 citation*
75. **[A Review of Incorporating Psychological Theories in LLMs](https://md.research.elamur.ai/free/en/papers/liu-2026-review-incorporating-psychological-theories-llms/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zizhou Liu, Ziwei Gong, Lin Ai, et al.*2026 · 1 citation*
76. **[ART: Adaptive Reasoning Trees for Explainable Claim Verification](https://md.research.elamur.ai/free/en/papers/wadhwa-2026-art-adaptive-reasoning-trees-explainable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sahil Wadhwa, Himanshu Kumar, Guanqun Yang, et al.*2026 · 1 citation*
77. **[Beyond Value Benchmarks: Measuring Value-Structure Alignment in Large Language Models via Symmetric Q-Sorts](https://md.research.elamur.ai/free/en/papers/zheng-2026-beyond-value-benchmarks-measuring-value/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingting Zheng, Yuqi Ren, Linhao Yu, et al.*2026 · 1 citation*
78. **[Chain-of-Thought as a Lens: Evaluating Structured Reasoning Alignment between Human Preferences and Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-chain-thought-as-lens-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Boxuan Wang, Zhuoyun Li, Xinmiao Huang, et al.*2026 · 1 citation*
79. **[From “Thinking” to “Justifying”: Aligning High-Stakes Explainability with Professional Communication Standards](https://md.research.elamur.ai/free/en/papers/qian-2026-thinking-justifying-aligning-high-stakes/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chen Qian, Yimeng Wang, Yu Chen, et al.*2026 · 1 citation*
80. **[Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards](https://md.research.elamur.ai/free/en/papers/pisano-2026-process-reward-models-meet-planning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Raffaele Pisano, Roberto Navigli*2026 · 1 citation*
81. **[SPD-Faith Bench: Diagnosing and Improving Faithfulness in Chain-of-Thought for Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/lv-2026-spd-faith-bench-diagnosing-improving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Weijiang Lv, Yaoxuan Feng, Xiaobo Xia, et al.*2026 · 1 citation*
82. **[The Thin Line Between Comprehension and Persuasion in LLMs](https://md.research.elamur.ai/free/en/papers/wynter-2026-thin-line-between-comprehension-persuasion/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Adrian de Wynter, Tangming Yuan*2026 · 1 citation*
83. **[A Simple and Efficient Learning-Style Prompting for LLM Jailbreaking](https://md.research.elamur.ai/free/en/papers/luo-2026-simple-efficient-learning-style-prompting/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xuan Luo, Yue Wang, Zefeng He, et al.*2026*
84. **[Beyond Simple Term Injection: Reasoning Models for Legal Translation in a Non-Dominant Language Variety](https://md.research.elamur.ai/free/en/papers/natale-2026-beyond-simple-term-injection-reasoning/) [Annual Conference of the European Association for Machine Translation (EAMT)](https://md.research.elamur.ai/free/en/venues/eamt/)**Paolo Di Natale, Elena Chiocchetti, Marlies Alber, et al.*2026*
85. **[Calibrating Inference Time Alignment with Sequence-level Risk Accumulation](https://md.research.elamur.ai/free/en/papers/tan-2026-calibrating-inference-time-alignment-sequence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shanwen Tan, Ziyang Dong, Wei Ju, et al.*2026*
86. **[Choose Your Lens: Multi-Perspective Value Alignment of Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/zhao-2026-choose-your-lens-multi-perspective/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Gejian Zhao, Hanzhou Wu, Xinpeng Zhang*2026*
87. **[Code over Words: Overcoming Semantic Inertia via Code-Grounded Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2026-code-over-words-overcoming-semantic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Manjie Xu, Isabella Yin, Xinyi Tu, et al.*2026*
88. **[Continuous Interpretive Steering for Scalar Diversity](https://md.research.elamur.ai/free/en/papers/cho-2026-continuous-interpretive-steering-scalar-diversity/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ye-eun Cho*2026*
89. **[Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards](https://md.research.elamur.ai/free/en/papers/yuan-2026-curing-miracle-steps-llm-mathematical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Youliang Yuan, Qiuyang Mang, Jingbang Chen, et al.*2026*
90. **[Deactivating Refusal Triggers: Understanding and Mitigating Overrefusal in Safety Alignment](https://md.research.elamur.ai/free/en/papers/xue-2026-deactivating-refusal-triggers-understanding-mitigating/) [Workshop on Trustworthy NLP](https://md.research.elamur.ai/free/en/venues/trustnlp/)**Zhiyu Xue, Zimo Qi, Guangliang Liu, et al.*2026*
91. **[DEFT: Demystifying VLN Failures via a Unified Dual-View Explainability Framework for LLM-based Agents](https://md.research.elamur.ai/free/en/papers/wang-2026-deft-demystifying-vln-failures-unified/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yawen Wang, Yihan Dai, Jianming Chen, et al.*2026*
92. **[Exploring Layer Activation Dynamic of CoT via Knowledge Probe](https://md.research.elamur.ai/free/en/papers/zhang-2026-exploring-layer-activation-dynamic-cot/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chuanxin Zhang, Jiajun Liu, Yao He, et al.*2026*
93. **[From Answers to Arguments: Toward Trustworthy Clinical Diagnostic Reasoning with Toulmin-Guided Curriculum Goal-Conditioned Learning](https://md.research.elamur.ai/free/en/papers/zhan-2026-answers-arguments-trustworthy-clinical-diagnostic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chen Zhan, Xiaoyu Tan, Gengchen Ma, et al.*2026*
94. **[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*
95. **[NSF-CoT: Neuro-Symbolic Formal Verification of Chain-of-Thought Faithfulness in Contextual Question Answering](https://md.research.elamur.ai/free/en/papers/pramanik-2026-nsf-cot-neuro-symbolic-formal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Vishal Pramanik, Maisha Maliha, Nathaniel D. Bastian, et al.*2026*
96. **[PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2026-pilot-planning-internalized-latent-optimization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoyu Zheng, Yun Zhu, Yuqian Yuan, et al.*2026*
97. **[PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations](https://md.research.elamur.ai/free/en/papers/wu-2026-prism-probing-reasoning-instruction-source/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuhe Wu, Guangyu Wang, Yuran Chen, et al.*2026*
98. **[Reasoning Hijacking: The Fragility of Reasoning Alignment in Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2026-reasoning-hijacking-fragility-reasoning-alignment/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuansen Liu, Yixuan Tang, Anthony Kum Hoe Tung*2026*
99. **[RELIC: Evaluating Complex Reasoning via the Recognition of Languages In-Context](https://md.research.elamur.ai/free/en/papers/petty-2026-relic-evaluating-complex-reasoning-recognition/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jackson Petty, Michael Y. Hu, Wentao Wang, et al.*2026*
100. **[S2H-DPO: Hardness-Aware Preference Optimization for Vision–Language Models](https://md.research.elamur.ai/free/en/papers/shukla-2026-s2h-dpo-hardness-aware-preference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nitish Shukla, Surgan Jandial, Arun Ross*2026*

**1** [2](https://md.research.elamur.ai/free/en/authors/miles-turpin/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/authors/miles-turpin/cited-by/page/2/)

- [← back to](https://md.research.elamur.ai/free/en/authors/miles-turpin/) — Miles Turpin