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> Wrong! The "Large Language Model" obviously lacks a coherent understanding of language. It's not all-or-nothing for the entirety of language, but yeah it's k

by Ukv 2y ago

> Wrong! The "Large Language Model" obviously lacks a coherent understanding of language. It's not all-or-nothing for the entirety of language, but yeah it's known that LLMs are imprecise at mapping from tokens to characters (as a human who only saw tokens may be, to be fair). For an unseen question, even just to follow the Q/A format and give a right-category answer already demonstrates some model of language, I'd argue. > "Language" is supposedly it's bread and butter but the "language" it produces is "correct" only from a statistical perspective. In other words, it simply can't be trusted. Nothing can be trusted 100%, and current LLMs are far below humans, but both the structure and the meaning of their language is correct significantly above a random-guessing baseline. A pretrained foundation model will have representations of fiction and non-fiction content, and can be guided towards either one with RL or a system prompt. > LLMs fundamentally turn computing on it's head. Instead of accurate results at low cost, they produce inaccurate results at high cost. Compared to what? LLMs often do things that traditional algorithms couldn't, faster and cheaper than hiring a human.

jqpabc123 2y ago
... demonstrates some model of language, I'd argue. Yes, it's model is all based on statistics. It lacks any real "coherent understanding". It produces nice prose --- that simply can't be trusted for even rudimentary results.
- Ukv 2y ago
  What do you think "real" understanding is? To me, if something can map some modality (the word "horse", a picture of a horse, ...) into corresponding representation in its internal semantic space then I'm fine to say it "understood" that. There are varying degrees to how useful an understanding is (does the semantic space link related concepts closely together? can it be used to reason, extract information, and make predictions?).
  - jqpabc123 2y ago
    ...can it be used to reason, extract information, and make predictions? Reason? Clearly no. It doesn't understand what you're asking well enough to "reason". But it will gladly offer an attempt based on some statistical mishmash of patterns it has been trained with. extract information, and make predictions? Yes --- just not reliably or accurately. Anything it produces needs verification --- which defeats a huge chunk of it's utility. Would you trust a calculator that fails a simple math test? Of course not. So why trust an LLM? Q: What is the square root of 2? A: approximately 1.41421356 Q: What is the square root of one plus one times forty five minus eighty eight? A: i * sqrt(42)
    - Ukv 2y ago
      > Reason? Clearly no. It doesn't understand what you're asking well enough to "reason". If you ask something a question ("Could a car fit in a bread-box?"), it maps from words into concepts in its internal semantic space ("car" to representation of car, associated with large size), then applies rules/patterns in a way that reaches a correct conclusion more often than an RNG baseline - what concretely would you say is missing from the process? > Would you trust a calculator that fails a simple addition test? Of course not. So why trust an LLM? You can weigh something in proportion to its accuracy, opposed to giving total trust or nothing at all. An 80%-accurate weather prediction is still useful, as are calculators despite potential for hardware issues or (more likely) fat-thumbing the wrong button. > Q: What is the square root of one plus one times forty five minus eighty eight? A: i * sqrt(42) I'm unclear on what you intend to be showing here. Is this an LLM response?
      - jqpabc123 2y ago
        Is this an LLM response? Yes. It shows how easily an LLM can misinterpret subtlety and ambiguity in human language.
        Ukv 2y ago
        > Yes. It shows how easily an LLM can misinterpret subtlety and ambiguity in human language. Without parentheses or context, -42 is a perfectly reasonable answer to "one plus one times forty five minus eighty eight", to the extent that it's probably what I'd go with if asked it on a quiz. I imagine when you read it in you're head you're putting emphasis/pauses in places that lean towards the `((1+1) * 45) - 88` interpretation, but neither I nor the LLM have that information. If asked, GPT-4 can give reasonable responses to this issue: https://i.imgur.com/ptMB7Ci.png https://i.imgur.com/ptMB7Ci.png

by Ukv 2y ago

jqpabc123 2y ago

... demonstrates some model of language, I'd argue. Yes, it's model is all based on statistics. It lacks any real "coherent understanding". It produces nice prose --- that simply can't be trusted for even rudimentary results.

Ukv 2y ago
What do you think "real" understanding is? To me, if something can map some modality (the word "horse", a picture of a horse, ...) into corresponding representation in its internal semantic space then I'm fine to say it "understood" that. There are varying degrees to how useful an understanding is (does the semantic space link related concepts closely together? can it be used to reason, extract information, and make predictions?).
- jqpabc123 2y ago
  ...can it be used to reason, extract information, and make predictions? Reason? Clearly no. It doesn't understand what you're asking well enough to "reason". But it will gladly offer an attempt based on some statistical mishmash of patterns it has been trained with. extract information, and make predictions? Yes --- just not reliably or accurately. Anything it produces needs verification --- which defeats a huge chunk of it's utility. Would you trust a calculator that fails a simple math test? Of course not. So why trust an LLM? Q: What is the square root of 2? A: approximately 1.41421356 Q: What is the square root of one plus one times forty five minus eighty eight? A: i * sqrt(42)
  - Ukv 2y ago
    > Reason? Clearly no. It doesn't understand what you're asking well enough to "reason". If you ask something a question ("Could a car fit in a bread-box?"), it maps from words into concepts in its internal semantic space ("car" to representation of car, associated with large size), then applies rules/patterns in a way that reaches a correct conclusion more often than an RNG baseline - what concretely would you say is missing from the process? > Would you trust a calculator that fails a simple addition test? Of course not. So why trust an LLM? You can weigh something in proportion to its accuracy, opposed to giving total trust or nothing at all. An 80%-accurate weather prediction is still useful, as are calculators despite potential for hardware issues or (more likely) fat-thumbing the wrong button. > Q: What is the square root of one plus one times forty five minus eighty eight? A: i * sqrt(42) I'm unclear on what you intend to be showing here. Is this an LLM response?
    - jqpabc123 2y ago
      Is this an LLM response? Yes. It shows how easily an LLM can misinterpret subtlety and ambiguity in human language.
      - Ukv 2y ago
        > Yes. It shows how easily an LLM can misinterpret subtlety and ambiguity in human language. Without parentheses or context, -42 is a perfectly reasonable answer to "one plus one times forty five minus eighty eight", to the extent that it's probably what I'd go with if asked it on a quiz. I imagine when you read it in you're head you're putting emphasis/pauses in places that lean towards the `((1+1) * 45) - 88` interpretation, but neither I nor the LLM have that information. If asked, GPT-4 can give reasonable responses to this issue: https://i.imgur.com/ptMB7Ci.png https://i.imgur.com/ptMB7Ci.png