Breaking the Chain: A Causal Analysis of LLM Faithfulness to Intermediate Structures
#large language models #faithfulness #intermediate reasoning #causal analysis #reliability #explainability #chain-of-thought
📌 Key Takeaways
- The study investigates how faithfully large language models (LLMs) follow intermediate reasoning steps.
- It uses causal analysis to assess the impact of these steps on final model outputs.
- Findings reveal inconsistencies in LLM adherence to provided reasoning chains.
- The research highlights potential reliability issues in LLM-generated explanations.
📖 Full Retelling
arXiv:2603.16475v1 Announce Type: new
Abstract: Schema-guided reasoning pipelines ask LLMs to produce explicit intermediate structures -- rubrics, checklists, verification queries -- before committing to a final decision. But do these structures causally determine the output, or merely accompany it? We introduce a causal evaluation protocol that makes this directly measurable: by selecting tasks where a deterministic function maps intermediate structures to decisions, every controlled edit impl
🏷️ Themes
AI Reliability, Causal Analysis
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Original Source
arXiv:2603.16475v1 Announce Type: new
Abstract: Schema-guided reasoning pipelines ask LLMs to produce explicit intermediate structures -- rubrics, checklists, verification queries -- before committing to a final decision. But do these structures causally determine the output, or merely accompany it? We introduce a causal evaluation protocol that makes this directly measurable: by selecting tasks where a deterministic function maps intermediate structures to decisions, every controlled edit impl
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