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# A Preliminary Exploration into Factored Cognition with Language Models

**[EleutherAI](https://daily.dev/sources/eleutherai)** · 7 min read · 0 upvotes · 0 comments

## Summary

EleutherAI researchers investigate factored cognition — breaking complex tasks into smaller subtasks — as a way to improve GPT-3's performance on arithmetic and math problems it cannot otherwise solve. Experiments cover nested expression evaluation (linear decomposition), branched nested function evaluation (exponential branching), and open-ended math word problems from MathQA. Results show that stepwise decomposition significantly outperforms direct answering, though accuracy degrades rapidly as task depth and branching increase. The MathQA experiments reveal GPT-3 still struggles with noisy, conceptually demanding word problems even with rationale-based few-shot prompting.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.eleuther.ai/factored-cognition>

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