Estimating Text Temperature with Language Models

Nikolay Mikhaylovskiy · Jan 5, 2026 · Citations: 0

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How to use this paper page

Coverage: Stale

Use this page to decide whether the paper is strong enough to influence an eval design. It summarizes the abstract plus available structured metadata. If the signal is thin, use it as background context and compare it against stronger hub pages before making protocol choices.

Best use

Background context only

Metadata: Stale

Trust level

Low

Signals: Stale

What still needs checking

Extraction flags indicate low-signal or possible false-positive protocol mapping.

Signal confidence: 0.25

Abstract

Autoregressive language models typically use temperature parameter at inference to shape the probability distribution and control the randomness of the text generated. After the text was generated, this parameter can be estimated using maximum likelihood approach. Following it, we propose a procedure to estimate the temperature of any text, including ones written by humans, with respect to a given language model. We evaluate the temperature estimation capability of a wide selection of small-to-medium Large Language Models (LLMs). We then use the best-performing Qwen3 14B to estimate temperatures of popular corpora, finding that while most measured temperatures are close to 1, notable exceptions include Jokes, GSM8K, and AG News (1.1), and Python code (0.9).

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Extraction flags indicate low-signal or possible false-positive protocol mapping.
Extraction confidence is 0.25 (below strong-reference threshold).
No explicit evaluation mode was extracted from available metadata.

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HFEPX Relevance Assessment

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

Background context only.

Main weakness

Extraction flags indicate low-signal or possible false-positive protocol mapping.

Trust level

Low

Eval-Fit Score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

HFEPX Fit

Adjacent candidate

Extraction confidence: Low

If you are doing eval pipeline work, start here:

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What This Page Found In The Paper

Each field below shows whether the signal looked explicit, partial, or missing in the available metadata. Use this to judge what is safe to trust directly and what still needs full-paper validation.

Human Feedback Types

missing

None explicit

Confidence: Low Not found

No explicit feedback protocol extracted.

Evidence snippet: Autoregressive language models typically use temperature parameter at inference to shape the probability distribution and control the randomness of the text generated.

Evaluation Modes

missing

None explicit

Confidence: Low Not found

Validate eval design from full paper text.

Evidence snippet: Autoregressive language models typically use temperature parameter at inference to shape the probability distribution and control the randomness of the text generated.

Quality Controls

missing

Not reported

Confidence: Low Not found

No explicit QC controls found.

Evidence snippet: Autoregressive language models typically use temperature parameter at inference to shape the probability distribution and control the randomness of the text generated.

Benchmarks / Datasets

partial

GSM8K

Confidence: Low Direct evidence

Useful for quick benchmark comparison.

Evidence snippet: We then use the best-performing Qwen3 14B to estimate temperatures of popular corpora, finding that while most measured temperatures are close to 1, notable exceptions include Jokes, GSM8K, and AG News (1.1), and Python code (0.9).

Reported Metrics

missing

Not extracted

Confidence: Low Not found

No metric anchors detected.

Evidence snippet: Autoregressive language models typically use temperature parameter at inference to shape the probability distribution and control the randomness of the text generated.

Rater Population

missing

Unknown

Confidence: Low Not found

Rater source not explicitly reported.

Evidence snippet: Autoregressive language models typically use temperature parameter at inference to shape the probability distribution and control the randomness of the text generated.

Human Data Lens

Uses human feedback: No
Feedback types: None
Rater population: Unknown
Unit of annotation: Unknown
Expertise required: Math, Coding
Signal basis: Structured extraction plus abstract evidence.

Evaluation Lens

Evaluation modes:
Agentic eval: None
Quality controls: Not reported
Signal confidence: 0.25
Known cautions: low_signal, possible_false_positive

Protocol And Measurement Signals

Benchmarks / Datasets

GSM8K

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

Autoregressive language models typically use temperature parameter at inference to shape the probability distribution and control the randomness of the text generated.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

Autoregressive language models typically use temperature parameter at inference to shape the probability distribution and control the randomness of the text generated.
After the text was generated, this parameter can be estimated using maximum likelihood approach.
Following it, we propose a procedure to estimate the temperature of any text, including ones written by humans, with respect to a given language model.

Researcher Actions

Compare this paper against others mentioning GSM8K.
Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
Use related-paper links to find stronger protocol-specific references.

Caveats

Generated from abstract + metadata only; no PDF parsing.
Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

GSM8K

Research Summary

Contribution Summary

Following it, we propose a procedure to estimate the temperature of any text, including ones written by humans, with respect to a given language model.
We evaluate the temperature estimation capability of a wide selection of small-to-medium Large Language Models (LLMs).

Why It Matters For Eval

Following it, we propose a procedure to estimate the temperature of any text, including ones written by humans, with respect to a given language model.

Researcher Checklist

Gap: Human feedback protocol is explicit

No explicit human feedback protocol detected.
Gap: Evaluation mode is explicit

No clear evaluation mode extracted.
Gap: Quality control reporting appears

No calibration/adjudication/IAA control explicitly detected.
Pass: Benchmark or dataset anchors are present

Detected: GSM8K
Gap: Metric reporting is present

No metric terms extracted.

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