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Temperature

Definition
AI-generated

Temperature scales logits before a softmax when sampling from a language model: values below one sharpen the distribution (more greedy), values above one flatten it (more random).

Topics
LLM and Agents
Related
Genome-Wide Association Study (GWAS) Large Language Model (LLM) Token

Why it matters in GWAS

Low temperature is often preferred for structured extraction of GWAS fields (allele labels, rsIDs) to reduce creative paraphrase; high temperature can diversify draft plain-language summaries but increases format drift.

Example usage

"We pinned temperature to 0.2 for the JSON slot-filling task and raised it only for the lay summary paragraph."

Genome-Wide Association Study (GWAS) Large Language Model (LLM) Token
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Ackley DH, Hinton GE, Sejnowski TJ. (1985). A learning algorithm for Boltzmann machines. Cogn Sci.
  • Brown T, et al. (2020). Language models are few-shot learners. NeurIPS.
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