Cover Image

Do Models Know Why They Changed Their Mind? Interpretability and Faithfulness of Chain-of-Thought Under Knowledge Conflict

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Venkata, Pruthvinath Jeripity
Format:	Preprint
Published:	2026
Subjects:	Computation and Language Artificial Intelligence Machine Learning I.2.7
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Three Regimes of Context-Parametric Conflict: A Predictive Framework and Empirical Validation
by: Venkata, Pruthvinath Jeripity
Published: (2026)

Why Models Know But Don't Say: Chain-of-Thought Faithfulness Divergence Between Thinking Tokens and Answers in Open-Weight Reasoning Models
by: Young, Richard J.
Published: (2026)

A Closer Look at Bias and Chain-of-Thought Faithfulness of Large (Vision) Language Models
by: Balasubramanian, Sriram, et al.
Published: (2025)

The Last Word Often Wins: A Format Confound in Chain-of-Thought Corruption Studies
by: Garcia, Gabriel
Published: (2026)

Evaluating the Efficacy of Hybrid Deep Learning Models in Distinguishing AI-Generated Text
by: Oketunji, Abiodun Finbarrs
Published: (2023)

Applied Explainability for Large Language Models: A Comparative Study
by: Kancharla, Venkata Abhinandan
Published: (2026)

AtManRL: Towards Faithful Reasoning via Differentiable Attention Saliency
by: Höth, Max Henning, et al.
Published: (2026)

Slim-SC: Thought Pruning for Efficient Scaling with Self-Consistency
by: Hong, Colin, et al.
Published: (2025)

Do Biased Models Have Biased Thoughts?
by: Rajwal, Swati, et al.
Published: (2025)

The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level
by: Herbst, Jeremy, et al.
Published: (2026)

NLP Case Study on Predicting the Before and After of the Ukraine-Russia and Hamas-Israel Conflicts
by: Miner, Jordan, et al.
Published: (2024)

Do Personality Traits Interfere? Geometric Limitations of Steering in Large Language Models
by: Bhandari, Pranav, et al.
Published: (2026)

Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures
by: Gao, Yutong, et al.
Published: (2026)

Prototype Transformer: Towards Language Model Architectures Interpretable by Design
by: Yordanov, Yordan, et al.
Published: (2026)

Large Language Model (LLM) Bias Index -- LLMBI
by: Oketunji, Abiodun Finbarrs, et al.
Published: (2023)

Adaptive Circuit Behavior and Generalization in Mechanistic Interpretability
by: Nainani, Jatin, et al.
Published: (2024)

Distilling Knowledge from Large Language Models: A Concept Bottleneck Model for Hate and Counter Speech Recognition
by: Labadie-Tamayo, Roberto, et al.
Published: (2025)

DynaSemble: Dynamic Ensembling of Textual and Structure-Based Models for Knowledge Graph Completion
by: Nandi, Ananjan, et al.
Published: (2023)

Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Driven Prolog-based Chain-of-Thought
by: Tan, Xiaoyu, et al.
Published: (2024)

RACE-Align: Retrieval-Augmented and Chain-of-Thought Enhanced Preference Alignment for Large Language Models
by: Yan, Qihang, et al.
Published: (2025)

Entropy-Based Measurement of Value Drift and Alignment Work in Large Language Models
by: Fadli, Samih
Published: (2025)

Knowledge Graph Embeddings: A Comprehensive Survey on Capturing Relation Properties
by: Niu, Guanglin
Published: (2024)

On Measuring Faithfulness or Self-consistency of Natural Language Explanations
by: Parcalabescu, Letitia, et al.
Published: (2023)

CodingTeachLLM: Empowering LLM's Coding Ability via AST Prior Knowledge
by: Chen, Zhangquan, et al.
Published: (2024)

The Geometry of Thought: How Scale Restructures Reasoning In Large Language Models
by: Anderson, Samuel Cyrenius
Published: (2026)

Control Reinforcement Learning: Interpretable Token-Level Steering of LLMs via Sparse Autoencoder Features
by: Cho, Seonglae, et al.
Published: (2026)

Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
by: Zhou, Tianyang, et al.
Published: (2026)

Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms
by: Hanna, Michael, et al.
Published: (2024)

Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory
by: Cacioli, Jon-Paul
Published: (2026)

Change Is the Only Constant: Dynamic LLM Slicing based on Layer Redundancy
by: Dumitru, Razvan-Gabriel, et al.
Published: (2024)

MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge
by: Lan, Guangchen, et al.
Published: (2025)

Revisiting Intermediate-Layer Matching in Knowledge Distillation: Layer-Selection Strategy Doesn't Matter (Much)
by: Yu, Zony, et al.
Published: (2025)

Large Language Models Can Better Understand Knowledge Graphs Than We Thought
by: Dai, Xinbang, et al.
Published: (2024)

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning
by: Mircea, Andrei, et al.
Published: (2025)

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression
by: Lasby, Mike, et al.
Published: (2025)

Random-Set Large Language Models
by: Mubashar, Muhammad, et al.
Published: (2025)

Improving Language Models with Intentional Analysis
by: Yin, Yuwei, et al.
Published: (2025)

Danoliteracy of Generative Large Language Models
by: Holm, Søren Vejlgaard, et al.
Published: (2024)

SWI: Speaking with Intent in Large Language Models
by: Yin, Yuwei, et al.
Published: (2025)

Uncovering Biases with Reflective Large Language Models
by: Chang, Edward Y.
Published: (2024)