251–260 of 412 posts
Earlier quoted context omitted.
Only in the same way that the plural of 'opinion' is 'fact' ;)
Except, very literally, data is a collection of single points (ie what we call "anecdotes").
Actual data is sampled randomly. Anecdotes very much are not.
Something that interests me about the Qwen and DeepSeek models is that they have presumably been trained to fit the worldview enforced by the CCP, for things like avoiding talking about Tiananmen Square - but we've had access to a range of Qwen/DeepSeek models for well over a year at this point and to my knowledge this assumed bias hasn't actually resulted in any documented problems from people using the models. Asid…
I think that depends what you do with the api. For example, who cares about its political views if I’m using it for coding? IMO politics is a minor portion of LLM use
I have a small physics-based problem I pose to LLMs. It's tricky for humans as well, and all LLMs I've tried (GPT o3, Claude 3.7, Gemini 2.5 Pro) fail to answer correctly. If I ask them to explain their answer, they do get it eventually, but none get it right the first time. Qwen3 with max thinking got it even more wrong than the rest, for what it's worth.
It's interesting that the release happened at 5am in China. Quite unusual.
Not that unusual in the context of trying to outshine anything that could be released tomorrow at llamacon.
Ask something like: "Ravioli: x = y: France, what could be x and y?" (it thought for 500s and the answers were "weird")
Or "Order from left to right these items ..." and give partial information on their relative position, eg Laptop is on the left of the cup and the cup is between the phone and the notebook. (Didn't have enough patience nor time to wait the thinking procedure for this)
I have a small physics-based problem I pose to LLMs. It's tricky for humans as well, and all LLMs I've tried (GPT o3, Claude 3.7, Gemini 2.5 Pro) fail to answer correctly. If I ask them to explain their answer, they do get it eventually, but none get it right the first time. Qwen3 with max thinking got it even more wrong than the rest, for what it's worth.
As they say, we shouldn't judge AI by the current state-of-the-art, but by how far and fast it's progressing. I can't wait to see future models get it even more wrong than that.
They keep being impressive at what they're good at (aggregating sources to solve a very well known problem) and terrible at what they're bad at (actually thinking through novel problems or old problems with few sources).
E.g. all ChatGPT, Claude and Gemini were absolutely terrible at generating Liquidsoap[0] scripts. It's not even that complex, but there's very little information to ingest about the problem space, so you can actually tell they are not "thinking".
Earlier quoted context omitted.
Aren't all Qwen models known to perform poorly with system prompt though?
I hadn't heard that, but it would certainly explain why the model made a mess of this task. Tried it again like this, using a regular prompt rather than a system prompt (with the https://github.com/simonw/llm-hacker-news plugin for the hn: prefix): llm -f hn:43825900 \ 'Summarize the themes of the opinions expressed here. For each theme, output a markdown header. Include direct "quotations" (with author attribution)…
Earlier quoted context omitted.
In my limited experience, models like Llama and Gemma are far more censored than Qwen and Deepseek.
Try to ask any model about Israel and Hamas
I dunno, these reasoning models seems kinda "dumb" because they try to bootstrap itself via reasoning, even though a simple direct answer might not exist (for example key information are missing for a proper answer). Ask something like: "Ravioli: x = y: France, what could be x and y?" (it thought for 500s and the answers were "weird") Or "Order from left to right these items ..." and give partial information on their…
I've had much better results from non-"reasoning" models by judging their output, doing actual reasoning myself, and then feeding new ideas back to them to steer the conversation. This too can go astray, as most LLMs tend to agree with whatever the human says, so this hinges on me being actually right.