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> Determinism is about producing the same output given the same input. In this sense, LLMs are fundamentally deterministic. You cannot formally verifiy prose o

by rvz 9mo ago

> Determinism is about producing the same output given the same input. In this sense, LLMs are fundamentally deterministic. You cannot formally verifiy prose or the text that LLMs generates when attempting to compare what a compiler does. So even in this sense that is completely false. No-one can guarrantee that the outputs will be 100% to what the instructions you are giving to the LLM, which is why you do not trust it. As long as it is made up of artificial neurons that predict the next token, it is fundamentally a stochastic model and unpredictable. One can maliciously craft an input to mess up the network to get the LLM to produce a different output or outright garbage. Compilers have reproducable builds and formal verification of their functionality. No such thing with LLMs exist. Thus, comparing LLMs to a compiler and suggesting that LLMs are 'fundamentally deterministic' or is even more than a compiler is completely absurd.

hackinthebochs 9mo ago
You're just using words incorrectly. Deterministic means repeatable. That's it. Predictable, verifiable, etc are tangential to deterministic. Your points are largely correct but you're not using the right words which just obfuscates your meaning.
- rvz 9mo ago
  Nope. You have not shown how a large scale collection of neural networks irrespective of their architecture is more deterministic when compared to a 'compiler' and only repeating a known misconception of tweaking the temperature to 0 which does not bring the determinism you claim it brings with LLMs [0] [1] [2], otherwise you would not have this problem in the first place. By even doing that, the result of the outputs are useless anyway. So this really does not help your point at all. So therefore: > You're just using words incorrectly. Deterministic means repeatable. That's it. Predictable, verifiable, etc are tangential to deterministic. There is nothing deteministic or predictable about an LLM even when you compare it to a compiler, unless you can guarrantee that the individual neurons through inference give a predictable output which would be useful enough for being a drop-in compiler replacement. [0] https://152334h.github.io/blog/non-determinism-in-gpt-4/ https://152334h.github.io/blog/non-determinism-in-gpt-4/ [1] https://arxiv.org/pdf/2506.09501 https://arxiv.org/pdf/2506.09501 [2] https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/ https://thinkingmachines.ai/blog/defeating-nondeterminism-in...
  - hackinthebochs 9mo ago
    Yes, there's some unknown sources of non-determinism when running production LLM architectures at full capacity. But that's completely irrelevant to the point. The core algorithm is deterministic. And you're still conflating deterministic and predictable. It's strange to have such disregard for the meaning of words and their correct usage.
    - rvz 9mo ago
      > Yes, there's some unknown sources of non-determinism when running production LLM architectures at full capacity. But that's completely irrelevant to the point. It is directly relevant and supports my whole point which just debunked your assertions on LLMs being ‘deterministic’ which doesn’t exist at a fundamental sense which you can’t guarantee that the behaviour and even the outputs will be the same. > The core algorithm is deterministic. And you're still conflating deterministic and predictable. The entire LLM is still non-deterministic and it is still considered to be unpredictable even if you take that to account. > It's strange to have such disregard for the meaning of words and their correct usage. Nope. Not only you have shown absolutely zero sources at all to prove the deterministic nature of LLMs to where it can function as a “compiler”, you ultimately conceded by agreeing with the linked paper(s) recognising that LLMs still do not have deterministic or predictable properties at all; even if you tweak the temp, parameters, etc. Therefore, once again LLMs are NOT compilers as even feeding them adversarial inputs can mess up the entire network up to become useless.
      - WithinReason 9mo ago
        from your first source: > As a consequence, the model is no longer deterministic at the sequence-level, but only at the batch-level therefore they are deterministic when the batch size is 1 Your second source lists a large number of ways how to make LLMs determnistic. The title of your third source is "Defeating Nondeterminism in LLM Inference" which also means that they can be made deterministic. Every single one of your sources proves you wrong, so no more sources need to be cited.

by rvz 9mo ago

hackinthebochs 9mo ago

You're just using words incorrectly. Deterministic means repeatable. That's it. Predictable, verifiable, etc are tangential to deterministic. Your points are largely correct but you're not using the right words which just obfuscates your meaning.

rvz 9mo ago
Nope. You have not shown how a large scale collection of neural networks irrespective of their architecture is more deterministic when compared to a 'compiler' and only repeating a known misconception of tweaking the temperature to 0 which does not bring the determinism you claim it brings with LLMs [0] [1] [2], otherwise you would not have this problem in the first place. By even doing that, the result of the outputs are useless anyway. So this really does not help your point at all. So therefore: > You're just using words incorrectly. Deterministic means repeatable. That's it. Predictable, verifiable, etc are tangential to deterministic. There is nothing deteministic or predictable about an LLM even when you compare it to a compiler, unless you can guarrantee that the individual neurons through inference give a predictable output which would be useful enough for being a drop-in compiler replacement. [0] https://152334h.github.io/blog/non-determinism-in-gpt-4/ https://152334h.github.io/blog/non-determinism-in-gpt-4/ [1] https://arxiv.org/pdf/2506.09501 https://arxiv.org/pdf/2506.09501 [2] https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/ https://thinkingmachines.ai/blog/defeating-nondeterminism-in...
- hackinthebochs 9mo ago
  Yes, there's some unknown sources of non-determinism when running production LLM architectures at full capacity. But that's completely irrelevant to the point. The core algorithm is deterministic. And you're still conflating deterministic and predictable. It's strange to have such disregard for the meaning of words and their correct usage.
  - rvz 9mo ago
    > Yes, there's some unknown sources of non-determinism when running production LLM architectures at full capacity. But that's completely irrelevant to the point. It is directly relevant and supports my whole point which just debunked your assertions on LLMs being ‘deterministic’ which doesn’t exist at a fundamental sense which you can’t guarantee that the behaviour and even the outputs will be the same. > The core algorithm is deterministic. And you're still conflating deterministic and predictable. The entire LLM is still non-deterministic and it is still considered to be unpredictable even if you take that to account. > It's strange to have such disregard for the meaning of words and their correct usage. Nope. Not only you have shown absolutely zero sources at all to prove the deterministic nature of LLMs to where it can function as a “compiler”, you ultimately conceded by agreeing with the linked paper(s) recognising that LLMs still do not have deterministic or predictable properties at all; even if you tweak the temp, parameters, etc. Therefore, once again LLMs are NOT compilers as even feeding them adversarial inputs can mess up the entire network up to become useless.
    - WithinReason 9mo ago
      from your first source: > As a consequence, the model is no longer deterministic at the sequence-level, but only at the batch-level therefore they are deterministic when the batch size is 1 Your second source lists a large number of ways how to make LLMs determnistic. The title of your third source is "Defeating Nondeterminism in LLM Inference" which also means that they can be made deterministic. Every single one of your sources proves you wrong, so no more sources need to be cited.