Do Models Know Why They Changed Their Mind? Interpretability and Faithfulness of Chain-of-Thought Under Knowledge Conflict
📰 ArXiv cs.AI
Learn how to evaluate the interpretability and faithfulness of chain-of-thought reasoning in language models under knowledge conflict and why it matters for trustworthy AI decisions
Action Steps
- Test the introspective faithfulness of your language model using the introduced framework
- Evaluate the stability of chain-of-thought reasoning across different prompt conditions
- Analyze the model's decision-making mechanism when faced with contradictory knowledge
- Apply the findings to improve the trustworthiness of language model decisions
- Compare the results across different models and datasets to identify trends and patterns
Who Needs to Know This
NLP researchers and engineers working on language models and interpretability can benefit from understanding how to assess the faithfulness of chain-of-thought reasoning in their models
Key Insight
💡 Chain-of-thought reasoning in language models can be unstable and unfaithful when faced with contradictory knowledge, highlighting the need for improved interpretability and evaluation methods
Share This
New research on interpretability and faithfulness of chain-of-thought reasoning in language models under knowledge conflict #AI #NLP
Key Takeaways
Learn how to evaluate the interpretability and faithfulness of chain-of-thought reasoning in language models under knowledge conflict and why it matters for trustworthy AI decisions
Full Article
Title: Do Models Know Why They Changed Their Mind? Interpretability and Faithfulness of Chain-of-Thought Under Knowledge Conflict
Abstract:
arXiv:2605.27773v1 Announce Type: cross Abstract: When a language model sees a document contradicting its training knowledge, it must choose: follow the document or trust itself. Prior work proved this choice depends on how well-known the fact is. We ask: does the model's chain-of-thought (CoT) reasoning faithfully report this mechanism? We introduce introspective faithfulness and test it across 200 questions, 8 models, and 4 prompt conditions. We find CoT reasoning is highly stable across oppos
Abstract:
arXiv:2605.27773v1 Announce Type: cross Abstract: When a language model sees a document contradicting its training knowledge, it must choose: follow the document or trust itself. Prior work proved this choice depends on how well-known the fact is. We ask: does the model's chain-of-thought (CoT) reasoning faithfully report this mechanism? We introduce introspective faithfulness and test it across 200 questions, 8 models, and 4 prompt conditions. We find CoT reasoning is highly stable across oppos
DeepCamp AI