Fascinating phenomenon. It's like a new Sapir–Whorf hypothesis. Do language models act differently in different languages due to those languages or the training materials?
So, part of its improved performance as they grow in parameter count is probably not only due to expanded raw material that it is trained upon, but a greater ability to ultimately ”realize” and connect apparent meanings of words, so that a German speaker might benefit more and more from training material in Korean.
> These results show that features at the beginning and end of models are highly language-specific (consistent with the {de, re}-tokenization hypothesis [31] ), while features in the middle are more language-agnostic. Moreover, we observe that compared to the smaller model, Claude 3.5 Haiku exhibits a higher degree of generalization, and displays an especially notable generalization improvement for language pairs that do not share an alphabet (English-Chinese, French-Chinese).
Source: https://transformer-circuits.pub/2025/attribution-graphs/bio...
However, they do see that Claude 3.5 Haiku seemed to have an English ”default” with more direct connections. It’s possible that a LLM needs to go a more roundabout way via generalizations to communicate in alternative languages and where this causes a dropoff in performance the smaller the model is?
It is like a student in school that is really brilliant in learning by heart, and repeating the words it studied, but not understanding the concept versus a student that actually understands the topic and can reason about the concepts.
My point is, those language pairs aren't random examples. Chinese isn't something completely foreign and new thing when it comes to difference between it and English.
It's clear from the start that language modelling is not yet there. It can't reason about low level structure (letters, syllables, rhyme, rhythm), it can't map all languages to a singular clear representation. Representation is mushy distributed mess out of which you get good or bad results.
It's brilliant how relevant the responses are and when they're correct, but the underlying process is driven by very weird internal representations.
Yep, that there seems like the definition of knowing. Don't worry, your humanity isn't at risk.
Knowing implies reasoning. LLMs don't "know" things. These statistical models continuate text. Having a mental model that they "know" things, that they can "reason" or "follow instructions" is driving all sorts of poor decisions.
Software has an abstraction fetish. So much of the material available for learners is riddled with analogies and "you don't need to know that" attitude. That is counter productive and I think having accurate mental models matters.
That's not really clear-cut, that's simply a position you're taking. JTB could (I reckon) say that a model's "knowledge" is justified by the training process and reward functions.
> LLMs don't "know" things. These statistical models continuate text.
I don't think it's clear to anyone at this point whether or not the steps taken before token selection (eg: the journey through their dimensional knowledge space provided by attention) are close to or far from how our own thought processes work, but the description of LLMs as "simply" continuating text reduces them to their outputs. From my perspective, as someone on the other side of a text-based web-app from you, you also are an entity that simply continuates text.
You have no way of knowing whether this comment was written by a sentient entity -- with thoughts and agency -- or an LLM.
And while accurate mental models can help in certain contexts, they're not always necessary. I don't need a detailed model of how my OS handles file operations to use it effectively. A high-level understanding is usually enough. Insisting on deep internal accuracy in every case seems more like gatekeeping than good practice.
There is a steep drop in quality in any non-English language, but in general less native speakers = worse results. They tend to have a certain "voice" which is extremely easy to spot and the accuracy of results goes out the window (way worse than in English).