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> "...and in general be careful when working with headers" I would love to know if there are benchmarks that show how much these prompts improve the responses.
by Ardren 1y ago
> "...and in general be careful when working with headers"
I would love to know if there are benchmarks that show how much these prompts improve the responses.
I'd suggest trying: "Be careful not to hallucinate." :-)
- bezier-curve 1y agoI'm thinking if the org that trained the model, and is doing interesting research of trying to understand how LLMs actually work on the inside [1], their caution might be warranted. [1] https://www.anthropic.com/research/tracing-thoughts-language-model https://www.anthropic.com/research/tracing-thoughts-language...
- swalsh 1y agoIn general, if you bring something up in the prompt most LLM's will bring special attention to it. It does help the accuracy of the thing you're trying to do. You can prompt an llm not to hallucinate, but typically you wouldn't say "don't hallucinate, you'd ask it to give a null value or say i don't know" which more closely aligns with the models training.
- Alifatisk 1y ago> if you bring something up in the prompt most LLM's will bring special attention to it How? In which way? I am very curious about this. Is this part of the transformer model or something that is done in the fine-tuning? Or maybe during the post-training?