In the paper DeepSeek just says they have ~800k responses that they used for the cold start data on R1, and are very vague about how they got it:
> To collect such data, we have explored several approaches: using few-shot prompting with a long CoT as an example, directly prompting models to generate detailed answers with reflection and verification, gathering DeepSeek-R1-Zero outputs in a readable format, and refining the results through post-processing by human annotators.
> Okay, so those are the problems. What’s the solution?
> If you need to perform a case mapping on a string, you can use LCMapStringEx with LCMAP_LOWERCASE or LCMAP_UPPERCASE, possibly with other flags like LCMAP_LINGUISTIC_CASING. If you use the International Components for Unicode (ICU) library, you can use u_strToUpper and u_strToLower.
Devaluing the new currency by adding lesser metals will also devalue existing currency that is "pure" as you aren't able to trust the value of the currency anymore, so the value of the existing pool of money will drop.
Its at a smaller scale, but it can be seen with counterfeit currency today. Cash-heavy businesses have to absorb whatever amount of counterfeits they accept, so they are really valuing your dollar at $0.99 if they might have to throw it out.
There are ways to gauge the confidence of the LLM (token probabilities over the response, generating multiple outputs and checking consistency), but yeah that’s outside the LLM itself. You could feed the info back to the LLM as a status/message I suppose
The ideal way is to release a security advisory that some CVE patches are coming with no details, so that upgrades can be prepped coordinated for a known patch day.
You just need to focus niche and upmarket, OpenAI is e.g. never going to make that "clone your chats and have your LLM-self go on pre-dates" app that went around Twitter.
It doesn’t need to be all or nothing though, humans are social creatures. If rates are similar, having 5-10% of your tribe being more exploratory could be advantageous, even if the rest are picking up slack in other areas.
Unsure if it’s pseudoscience, but similar to the argument around morning vs. evening preference being related to having someone alert for the tribe at the right times.
Outperform is dependent on the RAG approach (and this would be a RAG approach anyways, you can already do this with smaller context sizes). A simplistic one, probably, but dumping in data that you don't need dilutes the useful information, so I would imagine there would be at least _some_ degradation.
But there is also the downside of "tuning" the RAG to return less tokens you will miss extra context that could be useful to the model.
From my (limited) reading it seems like this is a UN treaty to allow for better co-operation between states when prosecuting/investigating cybercrime.
The main issues I see is that there is little distinction in what constitutes a crime that would fit under the "cybercrime" banner, potentially allowing a state that has e.g. anti-LGBT laws to request assistance in prosecuting that crime by e.g. getting de-anonymized data from a US-based social network.
That confused be a bit at first too, but that ranking seems to be for how widely they are used (not sure the exact metric), not the ranking of how good they are (which is the dbms of the year page)
> To collect such data, we have explored several approaches: using few-shot prompting with a long CoT as an example, directly prompting models to generate detailed answers with reflection and verification, gathering DeepSeek-R1-Zero outputs in a readable format, and refining the results through post-processing by human annotators.