Decompose questions into subquestions for more faithful reasoning
Break down complex questions into smaller, simpler subquestions to improve LLM reasoning faithfulness.
Decompose questions into subquestions to improve the faithfulness of model-generated reasoning.
By forcing the model to answer simpler subquestions in separate contexts, the faithfulness of model-generated reasoning is greatly increased compared to standard Chain-of-Thought (CoT). This method can still achieve some of the performance gains of CoT.
Instead of asking 'Explain the impact of quantum computing on cryptography', decompose it into: 'What is quantum computing?', 'What is current cryptography?', 'How might quantum computers break current cryptography?', 'What are quantum-resistant cryptography methods?'
Decompose complex questions into a series of simpler subquestions. This technique increases the accuracy and trustworthiness of the LLM's reasoning process, making it easier to verify its outputs, while still retaining performance benefits.
Answering simpler, isolated subquestions reduces the cognitive load on the model and makes it harder for errors or hallucinations to propagate through the reasoning chain.
- Question Decomposition Improves the Faithfulness of Model-Generated Reasoning ↗Anthropic Research · Anthropic Research · Dec 19, 2024 · paraphrased
First seen on Subvolts Dec 19, 2024. Updated Sep 11, 2026. Spotted a problem? Tell us.
- Question Decomposition Improves the Faithfulness of Model-Generated ReasoningAnthropic · Anthropic Research · Dec 19, 2024
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
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Questions people ask
- Which AI tools does this work with?
- Any current model. It was seen with in the sources.
- Where did this come from?
- Anthropic Research (Anthropic Research). Subvolts collected, normalized and credited it; the link above opens the original.