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LLM temperature zero setting does not guarantee deterministic output

By PulseAugur Editorial · [1 sources] ·

A recent experiment revealed that setting a large language model's temperature to zero and using a fixed seed does not guarantee deterministic output. When using Ollama with the llama3.1:8b model, identical prompts and settings produced different story outputs on the sixth request, although subsequent requests reverted to the original text. This suggests that subtle differences in floating-point computations during token scoring, influenced by factors like GPU kernels or operation order, can lead to variations even when sampling is disabled. AI

IMPACT Highlights potential issues with reproducibility in LLM outputs, impacting AI agent reliability and research.

RANK_REASON The item describes an experiment and observation about LLM behavior, not a new model release or product launch. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM temperature zero setting does not guarantee deterministic output

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54 / 100
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The item describes an experiment and observation about LLM behavior, not a new model release or product launch. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Josep Oriol Carné ·

    Temperature Zero, Fixed Seed, Different Answers

    <p>I was discussing reliability in AI agents when a familiar question came up: if you set an LLM's temperature to zero, doesn't that make it deterministic?</p> <p>It sounds reasonable. Remove random sampling, always pick the most likely next token, and you get the same answer eve…

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