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Fact-checking LLM outputs with self-ask
| Date | Stars |
|---|---|
| 2026-07-31 | 311 |
| 2026-08-06 | 311 |
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# fact checking with prompt chaining This repo is a simple demonstration of doing fact-checking with prompt chaining. How it works: - you ask your desired LLM a question - the LLM generates an initial answer to the question - the LLM self-interrogates what the assumptions were that went into that answer - the LLM sequentially determines if each of these assumptions are true - the LLM generates a new answer to the question, incorporating the new information ## to run Run `python3 fact_checker.py 'insert question here'` *Be sure to wrap your question in quotes if you're passing it as a command line argument.* Alternatively, you can use the provided `fact_checker.ipynb` notebook. ## example Question: *"What type of mammal lays the biggest eggs?"* **Initial answer:** The biggest eggs laid by any mammal belong to the elephant. **Assumptions made:** - The elephant is a mammal - Mammals lay eggs - Eggs come in different sizes - Elephants lay bigger eggs than other mammals **Verification of assumptions:** - The elephant is a mammal: TRUE - Mammals lay eggs: FALSE - Most mammals give birth to live young. - Eggs come in different sizes: TRUE - Elephants lay bigger eggs than other mammals: FALSE - Elephants do not lay eggs. **New answer:** This question cannot be answered because elephants do not lay eggs and most mammals give birth to live young. ## credits Proof of concept by [Jasper](https://twitter.com/0xjasper)
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ca8433b062dc3e66, topic:llm