Self-Interpretability: LLMs Can Describe Complex Internal Processes that Drive Their Decisions, and Improve with Training

Self-Interpretability: LLMs Can Describe Complex Internal Processes that Drive Their Decisions, and Improve with Training

21 May 2025

Dillon Plunkett

Papers citing "Self-Interpretability: LLMs Can Describe Complex Internal Processes that Drive Their Decisions, and Improve with Training"

16 / 16 papers shown

Title
Reasoning Models Don't Always Say What They Think Yanda Chen Joe Benton Ansh Radhakrishnan Jonathan Uesato Carson E. Denison ... Vlad Mikulik Samuel R. Bowman Jan Leike Jared Kaplan E. Perez ReLM LRM 122 37 1 08 May 2025
Metacognition and Uncertainty Communication in Humans and Large Language Models Mark Steyvers Megan A.K. Peters 47 2 0 18 Apr 2025
Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation Bowen Baker Joost Huizinga Leo Gao Zehao Dou M. Guan Aleksander Mądry Wojciech Zaremba J. Pachocki David Farhi LRM 136 26 0 14 Mar 2025
Looking Inward: Language Models Can Learn About Themselves by Introspection Felix J Binder James Chua Tomek Korbak Henry Sleight John Hughes Robert Long Ethan Perez Miles Turpin Owain Evans KELM AIFin LRM 64 16 0 17 Oct 2024
A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models Daking Rai Yilun Zhou Shi Feng Abulhair Saparov Ziyu Yao 140 31 0 02 Jul 2024
Mechanistic Interpretability for AI Safety -- A Review Leonard Bereska E. Gavves AI4CE 92 149 0 22 Apr 2024
Black-Box Access is Insufficient for Rigorous AI Audits Stephen Casper Carson Ezell Charlotte Siegmann Noam Kolt Taylor Lynn Curtis ... Michael Gerovitch David Bau Max Tegmark David M. Krueger Dylan Hadfield-Menell AAML 104 88 0 25 Jan 2024
Towards Evaluating AI Systems for Moral Status Using Self-Reports Ethan Perez Robert Long ELM 48 10 0 14 Nov 2023
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions Lei Huang Weijiang Yu Weitao Ma Weihong Zhong Zhangyin Feng ... Qianglong Chen Weihua Peng Xiaocheng Feng Bing Qin Ting Liu LRM HILM 105 855 0 09 Nov 2023
Bias and Fairness in Large Language Models: A Survey Isabel O. Gallegos Ryan Rossi Joe Barrow Md Mehrab Tanjim Sungchul Kim Franck Dernoncourt Tong Yu Ruiyi Zhang Nesreen Ahmed AILaw 84 574 0 02 Sep 2023
Faithfulness Tests for Natural Language Explanations Pepa Atanasova Oana-Maria Camburu Christina Lioma Thomas Lukasiewicz J. Simonsen Isabelle Augenstein FAtt 72 65 0 29 May 2023
Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting Miles Turpin Julian Michael Ethan Perez Sam Bowman ReLM LRM 68 427 0 07 May 2023
Language Models (Mostly) Know What They Know Saurav Kadavath Tom Conerly Amanda Askell T. Henighan Dawn Drain ... Nicholas Joseph Benjamin Mann Sam McCandlish C. Olah Jared Kaplan ELM 104 809 0 11 Jul 2022
Is Power-Seeking AI an Existential Risk? Joseph Carlsmith ELM 58 87 0 16 Jun 2022
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness? Alon Jacovi Yoav Goldberg XAI 100 596 0 07 Apr 2020
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 83 1,858 0 31 May 2018