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Empirical study on LLM output consistency in regulated financial tasks (RAG, JSON, SQL). Governance focus: Smaller models (Qwen2.5-7B, Granite-3-8B) hit 100% de

• colechristensen 11mo ago
  Outputs not being deterministic with temperature = 0 doesn't match my understanding of what "temperature" meant, I thought the definition of T=0 was determinism. Is this perhaps inference implementation details somehow introducing randomness?
  • kakugawa 11mo ago
    Defeating Nondeterminism in LLM Inference https://news.ycombinator.com/item?id=45200925 https://news.ycombinator.com/item?id=45200925 https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/ https://thinkingmachines.ai/blog/defeating-nondeterminism-in... > As it turns out, our request’s output does depend on the parallel user requests. Not because we’re somehow leaking information across batches — instead, it’s because our forward pass lacks “batch invariance”, causing our request’s output to depend on the batch size of our forward pass. tl;dr: the way inference is batched introduces non-determinism.
• doctorpangloss 11mo ago
  “Determinism is necessary for compliance” Says who? The stuff you comply with changes in real time. How’s that for determinism?
  • raffisk 11mo ago
    Author here—fair point, regs are a moving target . But FSB/BIS/CFTC explicitly require reproducible outputs for audits (no random drift in financial reports). Determinism = traceability, even when rules update at the very least Most groups I work with stick to traditional automation/rules systems, but top-down mandates are pushing them toward frontier models for general tasks—which then get plugged into these workflows. A lot stays in sandbox, but you'd be surprised what's already live in fin services. The authorities I cited (FSB/BIS/CFTC) literally just said last month AI monitoring is "still at early stage" cc https://www.fsb.org/2024/11/the-financial-stability-implications-of-artificial-intelligence/ https://www.fsb.org/2024/11/the-financial-stability-implicat... Curious how you'd tackle that real-time changing reg?
    • raffisk 11mo ago
      * https://www.fsb.org/2025/10/monitoring-adoption-of-artificial-intelligence-and-related-vulnerabilities-in-the-financial-sector/ https://www.fsb.org/2025/10/monitoring-adoption-of-artificia... This was the link I meant from Oct ‘25 reiterating early stages of AI monitoring
  • nomel 11mo ago
    Also, what happens if you add a space to the end of the prompt? Or write a 12.00 to 12.000?
    • raffisk 11mo ago
      Good q—spacing could mess with tokenization, untested but def plausible. Worth a quick test on the setup - through the code for the fin svcs harness for tinkering / testing diff prompts/model arch’s based on feedback https://github.com/ibm-client-engineering/output-drift-financial-llms https://github.com/ibm-client-engineering/output-drift-finan...
  • ulrashida 11mo ago
    Please give an example of a statutory compliance item that "changes in real time". That's not the way regulations work. Your compliance is measured against a fixed version of legislation.
• throwdbaaway 11mo ago
  It is the reasoning. During the reasoning process, the top few tokens have very similar or even same logprobs. With gpt-oss-120b, you should be able to get deterministic output by turning off reasoning, e.g. by appending: {"role": "assistant", "content": "<think></think>"} Of course, the model will be less capable without reasoning.
  • raffisk 11mo ago
    Good call—reasoning token variance is likely a factor, esp with logprob clustering at T=0. Your <think></think> workaround would work, but we need reasoning intact for financial QA accuracy. Also the mistral medium model we tested had ~70% deterministic outputs across the 16 runs for the text to sql gen and summarization in json tasks- and it had reasoning on. Llama 3.3 70b started to degrade and doesn’t have reasoning. But it’s a relevant variable to consider