this post was submitted on 01 Jun 2025
261 points (96.1% liked)
Technology
71042 readers
3691 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related news or articles.
- Be excellent to each other!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, this includes using AI responses and summaries. To ask if your bot can be added please contact a mod.
- Check for duplicates before posting, duplicates may be removed
- Accounts 7 days and younger will have their posts automatically removed.
Approved Bots
founded 2 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
It's true that LLMs aren't "aware" of what internal steps they are taking, so asking an LLM how they reasoned out an answer will just output text that statistically sounds right based on its training set, but to say something like "they can never reason" is provably false.
Its obvious that you have a bias and desperately want reality to confirm it, but there's been significant research and progress in tracing internals of LLMs, that show logic, planning, and reasoning.
EDIT: lol you can downvote me but it doesn't change evidence based research
Developing a AAA video game has a higher carbon footprint than training an LLM, and running inference uses significantly less power than playing that same video game.
Too deep on the AI propaganda there, it’s completing the next word. You can give the LLM base umpteen layers to make complicated connections, still ain’t thinking.
The LLM corpos trying to get nuclear plants to power their gigantic data centers while AAA devs aren’t trying to buy nuclear plants says that’s a straw man and you simultaneously also are wrong.
Using a pre-trained and memory-crushed LLM that can run on a small device won’t take up too much power. But that’s not what you’re thinking of. You’re thinking of the LLM only accessible via ChatGPT’s api that has a yuge context length and massive matrices that needs hilariously large amounts of RAM and compute power to execute. And it’s still a facsimile of thought.
It’s okay they suck and have very niche actual use cases - maybe it’ll get us to something better. But they ain’t gold, they ain't smart, and they ain’t worth destroying the planet.
Facts disagree, but you've decided to live in a reality that matches your biases despite real evidence, so whatever 👍
It’s literally tokens. Doesn’t matter if it completes the next word or next phrase, still completing the next most likely token 😎😎 can’t think can’t reason can witch’s brew facsimile of something done before
Why aren't they tokens when you use them? Does your brain not also choose the most apt selection for the sequence to make maximal meaning in the context prompted? I assert that after a sufficiently complex obfuscation of the underlying mathematical calculations the concept of reasoning becomes an exercise in pedantic dissection of the mutual interpretation of meaning. Our own minds are objectively deterministic, but the obfuscation provided by lack of direct observation provides the quantum cover fire needed to claim we are not just LLM equivalent representation on biological circuit boards.
would there be a source for such research?
https://www.anthropic.com/research/tracing-thoughts-language-model for one, the exact article OP was asking for
but this article espouses that llms do the opposite of logic, planning, and reasoning?
quoting:
are there any sources which show that llms use logic, conduct planning, and reason (as was asserted in the 2nd level comment)?
No, you're misunderstanding the findings. It does show that LLMs do not explain their reasoning when asked, which makes sense and is expected. They do not have access to their inner-workings and generate a response that "sounds" right, but tracing their internal logic shows they operate differently than what they claim, when asked. You can't ask an LLM to explain its own reasoning. But the article shows how they've made progress with tracing under-the-hood, and the surprising results they found about how it is able to do things like plan ahead, which defeats the misconception that it is just "autocomplete"
ollama is not an LLM, but a program used to run them. What model are you running?
Yes I'm well aware thank you.
Gemma3 was latest when I installed it.
I've been very unimpressed by gemma3. 1b, 4b, 12b? 27b is probably your best chance at coherent results. Try qwen3:32b