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The output of an LLM is often qualitative, not quantitative, and to test that, you need something that can judge the quality.

You're not debating philosophy with the LLM, you're just asking it if the answer matches (semantically) to the expected one.

I usually test LLM output quality with the following prompt (simplified):

"An AI assistant was tasked with {task}. The relevant information for their task was {context}. Their answer is {answer}. The correct answer should be something like {ground truth}. Is their answer correct?"

Then you can spice it up with chain of thought, asking it to judge alongside preferred criteria/dimensions and output a score, etc... you can go as wild as you'd like. But even this simple approach tends to work really well.

> turtles all the way down.

Saying "LLM testing LLM" is bad is like saying "computer testing computer" is bad. Yet, automated tests have value. And just as the unit tests will not prove your program is bug free, LLM evals won't guarantee 100% correctness. But they're incredibly useful tool.

In my experience working on pretty complex multi-agent multi-step systems, trying to get those to work without an eval framework in place is like playing whack-a-mole, only way less fun.

> "An AI assistant was tasked with {task}. The relevant information for their task was {context}. Their answer is {answer}. The correct answer should be something like {ground truth}. Is their answer correct?"

If you have a ground truth, what was the purpose of asking the AI assistant for an answer in the first place?

The cat is dead. The cat is no longer alive. These are equivalent enough, usually, but fails string comparison.

Or like of you did ai voice calling the goal was XYZ did the conversation get, in so many words, to XYZ?

When you're writing a test, you usually know the correct answer for that specific combination of input parameters.

Looking back at it, I must have been very tired when I wrote that!

Or maybe I was thinking about cases where the ground truth is difficult to establish.

Too late to edit, but here's a great, really in-depth post about using LLMs as judges to evaluate LLM outputs (when you don't have the ground truth for everything): https://cameronrwolfe.substack.com/p/finetuned-judge This is about finetuning LLMs to do it, but the first part is a good intro to why and how.

See von neumanns work on reliable machines from unreliable processes.

I wouldn't doubt that if each layer of an LLM added some additional check for an unreliable process that eventually you can make something reliable from the unreliable.

https://news.ycombinator.com/item?id=10177396

https://www.cs.unm.edu/~saia/talks/reliable-from-unreliable....

I don't see how von Neumann's work here helps at eliminating the problem and is arguably not particularly different than "just use more LLMs". His key result was to come up with a sufficient number of redundant computations to get the error below a threshold, which is still unreliable. This problem is worse because the fundamental issue is even trying to quantify what "correct" means.

Your suggestion at evaluating accuracy at the layers level necessarily implies there's some method of quantifiably detecting hallucinations. This is not necessarily possible given the particular attention models or even what is mathematically possible given an "infer this from finite text and no ability for independent verification"

> Who knows ¯\_(ツ)_/¯ WFT is that random pile of punctuation marks in the middle of that sentence... does the LLM even have a token for that?

  $ ollama run llama3.2
  >>> What does ¯\_(ツ)_/¯ mean?
  A popular internet meme!

  ¯\_(ツ)_/¯ is a Unicode character that represents a face with a
  surprised or confused expression. The three parts of the character are
  often interpreted as:

  1. `¯` - a horizontal bar, indicating skepticism or "not buying it"
  2. `(ツ)` - a smiling face with a tongue-out gesture, showing amusement
  or frustration
  3. `/¯` - an arrow pointing downwards, suggesting "that's just not
  right" or "good riddance"

  Together, the character is often used to express exasperation,
  disappointment, or sarcasm in response to a situation that seems
  ridiculous, unfair, or annoying. It's commonly used in text messages,
  social media posts, and online forums.

  Example: "You think you can eat an entire pizza by yourself?¯\_(ツ)_/¯"

  >>> /clear
  Cleared session context
  >>> ¯\_(ツ)_/¯
  ¯\_(ツ)_/¯

  >>> ¯\_(ツ)_/¯
  I see you're trying to express a mix of sass and Japanese flair! Keep
  'em coming, I'll keep responding in kind!

  >>> ¯\_(ツ)_/¯
  The eternal "shrug emoji" of the internet. You win, you've conveyed an
  entire emotional spectrum with just three characters. Well played,
  friend!

  >>> ¯\_(ツ)_/¯
  I think we've reached a new level of emoji sophistication. You're not
  even trying to convey anything anymore; it's just a masterclass in
  subtlety and understatement. Bravo!

Sometimes it’s nonsense, sometimes it’s approximately right (“shrug emoji”), sometimes it barks up the wrong tree (“Japanese flair”), sometimes it’s just vapid drivel.

> barks up the wrong tree (“Japanese flair”)

It just knows more than you. Google says:

katakana letter tu (U+30C4) - ツ

I used the expression “barks up the wrong tree” on purpose. I know the character is Japanese, but that’s not how it’s being used.

it's an "eastern" style emoticon https://en.wikipedia.org/wiki/List_of_emoticons#Eastern though they're more common in the west nowadays (but not enough for your virtual keyboards to have them built in...)