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LLM sampling parameter order silently alters model output

By PulseAugur Editorial · [1 sources] ·

The order in which sampling parameters like temperature, top_p, and top_k are applied significantly impacts the output of large language models. Two common stacking orders exist: temperature first, then top_k, then top_p; or top_k, then top_p, then temperature. Applying temperature before top_p alters the cumulative mass that top_p measures, leading to different token selections. This difference is often silent, meaning settings ported between different model implementations or configurations may not yield the expected results due to differing parameter application orders. AI

IMPACT Understanding the order of sampling parameters is crucial for reproducible LLM results and accurate replication of research findings.

RANK_REASON The item discusses a technical detail about LLM sampling parameters and their implementation, which is a research-level topic. [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 sampling parameter order silently alters model output

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42 / 100
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The item discusses a technical detail about LLM sampling parameters and their implementation, which is a research-level topic. [lever_c_demoted from research: ic=1 ai=1.0]
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model release
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Devanshu Biswas ·

    The Same Temperature, top_p and top_k Do Not Mean the Same Thing in Two Mainstream Stacks

    <p>Every prompt is sent with three numbers beside it, and the argument about them is always about <strong>which values to pick</strong>.</p> <p>This is about what that argument assumes away: the values are applied in an <strong>order</strong>, <strong>two orders ship in mainstrea…

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