Dollar General and Family Dollar are smaller stores that are generally the only option within a reasonable travel distance. Here in the South, you might be able to catch a bus to Wal-Mart, but it’ll take 2-3X more time (1 hour instead of 20 minutes), so people go with the closer option even thought it is more expensive. No guarantees that Wal-Mart will be cheaper either.
That’s because LLM’s generally don’t cite their sources. Web search is a tool outside of the LLM. Depending on the particular chat interface, there are any manner of tools in place to augment LLM capabilities/outputs, and they are constantly changing.
If one is trying to make an argument about the usefulness of LLMs, it’s irrelevant whether LLMs on their own can cite sources. If they can be trivially put into a system that can cite sources, that is a better measure of it’s usefulness.
I mean, it’s not trivial. There is a lot of work involved with enabling tool use at scale so that it works most of the time. Hiding that work makes it worse for the common user, because they aren’t necessarily going to understand the difference between platforms.
I agree that this is mostly OpenAI’s fault, though I also think people posting strong claims about LLMs online have a responsibility to know slightly more than the average user.
If you read the model card, Qwen3-Next can be extended to 1M context length with YaRN.
> Qwen3-Next natively supports context lengths of up to 262,144 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 1 million tokens using the YaRN method.
> If you read the model card, Qwen3-Next can be extended to 1M context length with YaRN.
I read the article, but as I said Qwen chat only provides up to 262k tokens in context length, so I'll stick with Qwen2.5 Turbo which supports 1M tokens.
Keyword alone sucks for negation. Searching a set of patient documents for “Which of my patients has COPD?” to get a set of responses that states “COPD not indicated” will not endear you to the physician using your tool. Hybrid (keyword + semantic) is much more all-encompassing.
Forwarding the users query directly to the document store seems ridiculous to me. The whole point is for the LLM to interpret the context and issue multiple targeted queries based upon the interpretation(s) arrived at.
The LLM is the semantic part. FTS is the keyword part. This is the hybrid you're looking for.
Sometimes you are searching for supporting evidence that is semantically related. COPD was just an example, you won’t get a direct keyword match if the Physician is searching for lung disease.
I mean, just this week there was a popular post “My AI Skeptic Friends are all Nuts” that generated a ton of such discussion and dismissal. https://news.ycombinator.com/item?id=44163063
I think that people are uncomfortable with the idea that Google Maps is centralized and can unilaterally change what you see. Having an offline version of a map helps protect against sudden change (go forward or retroactive).
As an aside, I do really like organic maps. I keep it installed with downloaded maps for when I travel to places with poor signal, including hiking trails.
I have used the Remarkable 2 for papers, but it is slightly too small to read text comfortably. I’m also an active reader, so I miss the color highlighting. Annotations are excellent. For now, I’m sticking to reviewing papers in the Zotero application on my iPad.
