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Comment by iknownothow | Hacker News Reader

Is there a terminology battle happening in some circles? And if so, what are the consequences of being wrong and using the wrong terminology?

I follow the rnd and progress in this space and I haven't heard anyone make a fuss about it. They are all LLMs or transformers or neural nets but they can be trained or optimized to do different things. For sure, there's terms like Reasoning models or Chat models or Instruct models and yes they're all LLMs.

But you can now start combining them to have hybrid models too. Are Omni models that handle audio and visual data still "language" models? This question is interesting in its own right for many reasons, but not to justify or bemoan the use of term LLM.

LLM is a good term, it's a cultural term too. If you start getting pedantic, you'll miss the bigger picture and possibly even the singularity ;)

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So there is a language war going on in the industry and some of its justified and some of its not. Take 'agents' as an example. I have seen an example of where a low code / no code service dropped in a LLM node in a 10+ year old product, started calling themselves an 'agent platform' and jacked up their price by a large margin. This is probably a case where a debate as to what qualifies as an 'agent' is appropriate.

Alternatively I have seen debates as to what counts as a 'Small Language Model' that probably are nonsensical. Particularly because in my personal language war the term 'small language model' shouldn't even exist (no one knows that the threshold is, and our 'small' language models are bigger than the 'large' language models from just a few years ago).

This is fairly typical of new technology. Marketing departments will constantly come up with new terms or try to take over existing terms to push agendas. Terms with defined meaning will get abused by casual participants and loose all real meaning. Individuals new to the field will latch on to popular misuses of terms as they try to figure out what everyone is talking about and perpetuate definition creep. Old hands will overly focus on hair splitting exercises that no one else really cares about and sigh in dismay as their carefully cultured taxonomies collapse under expansion of interest in their field.

It will all work itself out in 10 years or so.

There is a reason why cars and computers are sold with specs. 0-60 time, fuel efficiency...

People need to know the performance they can expect from LLMs or agents. What are they capable of?

A 2009 honda civic can get an under-5 seconds 0-60 easily... however it does involve high a cliff.

Result Specs (as in measuring output/experimental results) need strict definitions to be useful and I think the current ones with have for LLMs are pretty weak. (mostly benchmarks that model one kind of interaction, and usually not any sort of useful interaction)

Seems like a textbook example of https://en.m.wikipedia.org/wiki/No_true_Scotsman

Well i don't see why we need to mangle the jargon. "Language model" has an old meaning from NLP (which still applies), as a computer model of language itself. Most commonly, a joint probability distribution over words or sequences of words, which is what LLMs are too. Prompted replies are literally conditional probability densities conditioned on the context you give it. "Foundation model" is a more general term I see a lot.

To say a model is "just a LLM" is to presumably complain that it has no added bells or whistles that someone thinks is required beyond the above statistical model. And maybe I missed the point, but the author seems to be saying "yes it's just a LLM, but LLMs are all you need".

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