Can Language Models Explain Their Own Classification Behavior?
Fuente:
arXiv
Saved in:
| Main Authors: | Sherburn, Dane, Chughtai, Bilal, Evans, Owain |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Language Models Can Predict Their Own Behavior
by: Ashok, Dhananjay, et al.
Published: (2025)
by: Ashok, Dhananjay, et al.
Published: (2025)
Training Language Models to Explain Their Own Computations
by: Li, Belinda Z., et al.
Published: (2025)
by: Li, Belinda Z., et al.
Published: (2025)
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs
by: Laine, Rudolf, et al.
Published: (2024)
by: Laine, Rudolf, et al.
Published: (2024)
Transformer Circuit Faithfulness Metrics are not Robust
by: Miller, Joseph, et al.
Published: (2024)
by: Miller, Joseph, et al.
Published: (2024)
Thought Crime: Backdoors and Emergent Misalignment in Reasoning Models
by: Chua, James, et al.
Published: (2025)
by: Chua, James, et al.
Published: (2025)
Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models
by: Ciaperoni, Martino, et al.
Published: (2026)
by: Ciaperoni, Martino, et al.
Published: (2026)
Subliminal Learning: Language models transmit behavioral traits via hidden signals in data
by: Cloud, Alex, et al.
Published: (2025)
by: Cloud, Alex, et al.
Published: (2025)
Can LLMs Guide Their Own Exploration? Gradient-Guided Reinforcement Learning for LLM Reasoning
by: Liang, Zhenwen, et al.
Published: (2025)
by: Liang, Zhenwen, et al.
Published: (2025)
Explain in Your Own Words: Improving Reasoning via Token-Selective Dual Knowledge Distillation
by: Kim, Minsang, et al.
Published: (2026)
by: Kim, Minsang, et al.
Published: (2026)
Linearization Explains Fine-Tuning in Large Language Models
by: Afzal, Zahra Rahimi, et al.
Published: (2026)
by: Afzal, Zahra Rahimi, et al.
Published: (2026)
Can Large Language Models Still Explain Themselves? Investigating the Impact of Quantization on Self-Explanations
by: Wang, Qianli, et al.
Published: (2026)
by: Wang, Qianli, et al.
Published: (2026)
Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health
by: Mamun, Abdullah, et al.
Published: (2024)
by: Mamun, Abdullah, et al.
Published: (2024)
Conditional misalignment: common interventions can hide emergent misalignment behind contextual triggers
by: Dubiński, Jan, et al.
Published: (2026)
by: Dubiński, Jan, et al.
Published: (2026)
Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions
by: Kariyappa, Sanjay, et al.
Published: (2024)
by: Kariyappa, Sanjay, et al.
Published: (2024)
Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning
by: Sani, Numair, et al.
Published: (2020)
by: Sani, Numair, et al.
Published: (2020)
Building Production-Ready Probes For Gemini
by: Kramár, János, et al.
Published: (2026)
by: Kramár, János, et al.
Published: (2026)
Explaining Time Series Classification Predictions via Causal Attributions
by: Alcaraz, Juan Miguel Lopez, et al.
Published: (2024)
by: Alcaraz, Juan Miguel Lopez, et al.
Published: (2024)
Fairness Definitions in Language Models Explained
by: Yin, Zhipeng, et al.
Published: (2024)
by: Yin, Zhipeng, et al.
Published: (2024)
Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings
by: Schwalbe, Gesina, et al.
Published: (2026)
by: Schwalbe, Gesina, et al.
Published: (2026)
Negation Neglect: When models fail to learn negations in training
by: Mayne, Harry, et al.
Published: (2026)
by: Mayne, Harry, et al.
Published: (2026)
Explaining Large Language Models with gSMILE
by: Dehghani, Zeinab, et al.
Published: (2025)
by: Dehghani, Zeinab, et al.
Published: (2025)
Can Biases in ImageNet Models Explain Generalization?
by: Gavrikov, Paul, et al.
Published: (2024)
by: Gavrikov, Paul, et al.
Published: (2024)
MEMENTO: Teaching LLMs to Manage Their Own Context
by: Kontonis, Vasilis, et al.
Published: (2026)
by: Kontonis, Vasilis, et al.
Published: (2026)
Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data
by: Treutlein, Johannes, et al.
Published: (2024)
by: Treutlein, Johannes, et al.
Published: (2024)
Angles Don't Lie: Unlocking Training-Efficient RL Through the Model's Own Signals
by: Wang, Qinsi, et al.
Published: (2025)
by: Wang, Qinsi, et al.
Published: (2025)
Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions
by: Wang, Jingtan, et al.
Published: (2024)
by: Wang, Jingtan, et al.
Published: (2024)
Can Language Models Use Forecasting Strategies?
by: Pratt, Sarah, et al.
Published: (2024)
by: Pratt, Sarah, et al.
Published: (2024)
BEACON: Behavioral Malware Classification with Large Language Model Embeddings and Deep Learning
by: Perera, Wadduwage Shanika, et al.
Published: (2025)
by: Perera, Wadduwage Shanika, et al.
Published: (2025)
To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models
by: Barbulescu, George-Octavian, et al.
Published: (2024)
by: Barbulescu, George-Octavian, et al.
Published: (2024)
Learnware of Language Models: Specialized Small Language Models Can Do Big
by: Tan, Zhi-Hao, et al.
Published: (2025)
by: Tan, Zhi-Hao, et al.
Published: (2025)
The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"
by: Berglund, Lukas, et al.
Published: (2023)
by: Berglund, Lukas, et al.
Published: (2023)
Shared Lexical Task Representations Explain Behavioral Variability In LLMs
by: Yang, Zhuonan, et al.
Published: (2026)
by: Yang, Zhuonan, et al.
Published: (2026)
Explaining Datasets in Words: Statistical Models with Natural Language Parameters
by: Zhong, Ruiqi, et al.
Published: (2024)
by: Zhong, Ruiqi, et al.
Published: (2024)
Explingo: Explaining AI Predictions using Large Language Models
by: Zytek, Alexandra, et al.
Published: (2024)
by: Zytek, Alexandra, et al.
Published: (2024)
Explaining Large Language Models Decisions Using Shapley Values
by: Mohammadi, Behnam
Published: (2024)
by: Mohammadi, Behnam
Published: (2024)
Toward Explaining Large Language Models in Software Engineering Tasks
by: Vitale, Antonio, et al.
Published: (2025)
by: Vitale, Antonio, et al.
Published: (2025)
Can Kernel Methods Explain How the Data Affects Neural Collapse?
by: Kothapalli, Vignesh, et al.
Published: (2024)
by: Kothapalli, Vignesh, et al.
Published: (2024)
CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
by: Xie, Shifeng, et al.
Published: (2025)
by: Xie, Shifeng, et al.
Published: (2025)
Benchmarking is Broken -- Don't Let AI be its Own Judge
by: Cheng, Zerui, et al.
Published: (2025)
by: Cheng, Zerui, et al.
Published: (2025)
Language Model Embeddings Can Be Sufficient for Bayesian Optimization
by: Nguyen, Tung, et al.
Published: (2024)
by: Nguyen, Tung, et al.
Published: (2024)
Similar Items
-
Language Models Can Predict Their Own Behavior
by: Ashok, Dhananjay, et al.
Published: (2025) -
Training Language Models to Explain Their Own Computations
by: Li, Belinda Z., et al.
Published: (2025) -
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs
by: Laine, Rudolf, et al.
Published: (2024) -
Transformer Circuit Faithfulness Metrics are not Robust
by: Miller, Joseph, et al.
Published: (2024) -
Thought Crime: Backdoors and Emergent Misalignment in Reasoning Models
by: Chua, James, et al.
Published: (2025)