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Thinking out loud... I don't think these problems can be solved. If you are going to do it anyway, I would suggest: - Using a RAG architecture on top of a data
by dweinus 2y ago
Thinking out loud... I don't think these problems can be solved. If you are going to do it anyway, I would suggest:
- Using a RAG architecture on top of a database of factual information. Wikipedia is probably your best bet. It is not 100% factual or correct either, but maybe as good as it gets. Scaling RAG to wikipedia size is not trivial, but I think it can be done.
- Prompting the LLM to cite its sources so people can fact-check the fact-checker
- Prompting the LLM to say it is unsure when something does not have a clear answer. I don't expect this to be reliable, but maybe somewhat better
- helloduck1234 2y agoYea ok, we already have the citing thing done, and are going to start working on the RAG architecture soon.
- CuriouslyC 2y agoThere's a whole art to prompting a LLM to say it's unsure. I need to write a blog post about this, it's deep.
- visarga 2y agoSample a bunch of LLMs with the same question, if they disagree much then they are unsure. You can even sample the same LLM with high enough temperature, text augmentations, different prompts or different demonstrations. When they are correct they say the same thing, but when they make mistakes, they make different ones. This only works for factual or reasoning tasks, but that's where it matters.
- codetrotter 2y agoBut how do you know if the LLMs agree, when all of them word the response differently For example LLM 1: Yes, it is true that fireworks were invented in China LLM 2: Fireworks were indeed invented in China
- Bjartr 2y agoAsk another model if the two statements are in agreement of course! ;)
- omneity 2y agoThis is trivially achievable with function calling, assuming the model you use supports this (which most models do at this point). Define a function `reportFactual(isFactual: boolean)` and you will get standardized, machine-readable answers to do statistics with.
- codetrotter 2y agoI’ve used function calls with OpenAI. But are there any good local LLMs that you can run with Ollama that support function calling?
- omneity 2y agoIf you expect an OpenAI compatible API to use function calls, I don't think Ollama supports it yet (to be confirmed). However you can do it yourself using the appropriate tokens for the model. I know that Llama3, various Mistrals and Command-R support function calling out of the box. Here are the tokens to achieve this in Mixtral 8x22 https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1#function-calling-and-special-tokens https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1... Pass function definitions in the system prompt.
- indigodaddy 2y agoI think llamafile supports openai compatible api.. https://github.com/Mozilla-Ocho/llamafile https://github.com/Mozilla-Ocho/llamafile
- inimino 2y agoSimpler yet, just tell the model "Reply with 'Yes' or 'No'."
- 2y ago
- netrap 2y agoYou don't have to solve it, you just have to try...