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arxiv: 2606.01451 · v1 · pith:XGZE3NZ2new · submitted 2026-05-31 · 💻 cs.CL

Before and After Temperature: A Distributional View of Creative LLM Generation

V. S. Raghu Parupudi , Harsha Ponnada , Aditi Kaushal , S. Shria Parupudi , Saiteja Dasari , Sahiti Bulusu This is my paper

Pith reviewed 2026-06-28 17:05 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM creativity evaluationsampling temperaturetoken distribution reshapingreference-free metricsincoherence detectionSpearman rank correlationcumulative mass width
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The pith

A feature tracking how temperature reshapes token distributions predicts LLM creativity ranks at Spearman rho 0.918.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that examining the effect of sampling temperature on an LLM's token probability distribution yields a stronger signal for creative quality than standard metrics. A single per-token feature extracted from this reshaping correlates at 0.918 with averaged LLM judges and 0.870 with human rankings across 500 prompts, exceeding the 0.76 ceiling of baselines such as perplexity, entropy, top-1 margin, and compression ratio. The signal works by detecting a sharp widening of the distribution and mass leakage at high temperature, which marks the incoherence regime. The feature does not distinguish the two lower coherent temperatures, leaving that separation to sequence-level analysis. The LLM and human ground truths align at rho 0.83, above inter-human agreement.

Core claim

A single per-token feature derived from how sampling temperature reshapes the model's token distribution before the next token is drawn predicts the within-prompt creativity rank at Spearman ρ=0.918 against an averaged gpt-4o / gemini-2.5-pro judge (n=500) and ρ=0.870 against a three-rater human-majority ranking (n=150). Each of four standard reference-free baselines tops out at |ρ|≈0.76. Mechanistically the advantage stems from the distributional signature at T=1.5 where cumulative-mass width n95(q) inflates from ~1 to ~131 tokens and post-temperature mass leaks off the pre-temperature top-90% plausible set by about 13 percentage points.

What carries the argument

The per-token feature derived from temperature-induced reshaping of the token distribution, specifically via cumulative-mass width n95(q) and mass leakage from the top-90% plausible set.

If this is right

  • The feature supplies a stronger reference-free signal than perplexity, entropy, top-1 margin, or compression for ranking creative generations.
  • High-temperature incoherence is identified by inflation of cumulative-mass width and leakage of probability mass outside the pre-temperature top-90% set.
  • Discrimination between the two coherent temperature regimes requires additional sequence-level features.
  • The LLM-judge and human rankings agree at rho 0.83, which exceeds the inter-human ceiling of 0.77.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Generation pipelines could monitor the per-token reshaping feature in real time to adjust temperature or reject incoherent continuations.
  • The same reshaping statistics might serve as a diagnostic for other sampling parameters such as top-p or repetition penalty.
  • Creativity evaluation could shift from post-hoc text analysis to inspection of the pre-sampling distribution at each step.
  • The approach invites tests on whether the feature generalizes across model families beyond Llama-3.1-8B-Instruct.

Load-bearing premise

The LLM-judge and human-majority rankings provide a stable ground truth for creativity rather than mainly reflecting coherence or incoherence.

What would settle it

Measure whether the feature still ranks generations correctly when high-temperature outputs are filtered to remain coherent or when low-temperature outputs are made deliberately repetitive while keeping creativity low.

Figures

Figures reproduced from arXiv: 2606.01451 by Aditi Kaushal, Harsha Ponnada, Sahiti Bulusu, Saiteja Dasari, S. Shria Parupudi, V. S. Raghu Parupudi.

Figure 1
Figure 1. Figure 1: Mean rank by temperature for the two LLM judges (a) and three human raters plus their majority (b). Lower is more creative (1=best of 3). Both panels show T=1.5 ranked last on essentially every prompt; the mid-vs-bland call between T=0.8 and T=0.3 is close on both panels. Human raters. On the 150-prompt subset, three independent raters ranked the responses under the same coherence-first rule as the LLM jud… view at source ↗
Figure 2
Figure 2. Figure 2: Mean per-prompt Spearman ρ for inter-human pairs (gray, top) and human￾majority vs each LLM judge (colored, bottom). The averaged-LLM judge (ρ=+0.832) beats the inter-human ceiling (ρ¯=+0.771, dotted line) by a comfortable margin [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Per-temperature mean values for the eight distributional features (top two rows, blue) and four literature baselines (bottom row, red). Every distributional feature step-changes at T=1.5 by one to three orders of magnitude. The mid-vs-bland call (T=0.8 vs T=0.3) is the closer one: the baselines show some shift between the two coherent temperatures, but not enough to translate into reliable within-prompt ra… view at source ↗
Figure 4
Figure 4. Figure 4: Within-prompt Spearman ρ for the top four distributional features (left, blue/orange) and four literature baselines (right, gray) against the two ground truths. The averaged-LLM column uses all 500 prompts; human-majority uses the 150-prompt subset. Top-1 margin’s negative sign is absorbed by reporting |ρ|. Our top feature beats the best baseline by +0.165 (averaged-LLM) and +0.110 (human-majority). featur… view at source ↗
read the original abstract

Reference-free evaluation of large language model (LLM) creativity relies on perplexity, entropy, and top-1 margin. We show that a much stronger signal lives one step earlier in the pipeline: in how sampling temperature \emph{reshapes} the model's token distribution before the next token is drawn. On Llama-3.1-8B-Instruct generations of 500 open-ended creative prompts at $T \in \{0.3, 0.8, 1.5\}$, a single per-token feature derived from this reshaping predicts the within-prompt creativity rank at Spearman $\rho{=}0.918$ against an averaged gpt-4o\,/\,gemini-2.5-pro judge ($n{=}500$) and $\rho{=}0.870$ against a three-rater human-majority ranking ($n{=}150$). Each of four standard reference-free baselines (self-perplexity, mean predictive entropy, top-1 margin, gzip compression ratio) tops out at $|\rho|\!\approx\!0.76$ on both ground truths: a gap of $+0.165$ on averaged-LLM and $+0.110$ on human-majority, both far larger than the spread among the baselines themselves. The two ground-truth panels agree with each other at $\rho{=}0.83$, above the inter-human ceiling of $\rho{=}0.77$, so the comparison is not bottlenecked by judge noise. Mechanistically, the win comes from a sharp distributional signature of the incoherence regime: at $T{=}1.5$ the cumulative-mass width $n_{95}(q)$ inflates from $\sim\!1$ to ${\sim}\!131$ tokens and post-temperature mass leaks off the pre-temperature top-$90\%$ plausible set by about $13$ percentage points. The per-token aggregates do not separate $T{=}0.8$ from $T{=}0.3$; discriminating the two coherent regimes is left to sequence-level features.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper claims that a single per-token feature extracted from how sampling temperature reshapes an LLM's pre-softmax token distribution provides a substantially stronger reference-free signal for ranking creative generations than standard baselines. On Llama-3.1-8B-Instruct outputs for 500 open-ended prompts at T in {0.3, 0.8, 1.5}, this feature achieves Spearman ρ=0.918 against averaged GPT-4o/Gemini-2.5-pro judgments and ρ=0.870 against three-rater human-majority rankings (n=150), outperforming self-perplexity, mean entropy, top-1 margin, and gzip ratio (all |ρ|≈0.76). The signal is attributed to a sharp increase in cumulative-mass width n95(q) and mass leakage at T=1.5; the feature does not separate the two coherent regimes.

Significance. If the central correlations hold after addressing interpretation issues, the work supplies a mechanistic distributional account of temperature effects that materially improves reference-free creativity evaluation. Strengths include the large performance gap over four established baselines, the fact that LLM-judge and human rankings agree at ρ=0.83 (above the inter-human ceiling of 0.77), and the explicit regime-separation analysis. These elements would constitute a useful contribution to evaluation methodology in creative text generation.

major comments (2)
  1. [Abstract] Abstract, paragraph on mechanistic signature and regime separation: the reported correlations are driven exclusively by the T=1.5 incoherence regime (n95(q) rising from ~1 to ~131 tokens and 13pp mass leakage), while the per-token aggregates explicitly do not separate T=0.3 from T=0.8. Because the ground-truth rankings may largely encode coherence penalties rather than creativity distinctions within coherent outputs, the claim that the feature measures creativity (rather than incoherence detection) requires additional evidence, such as within-regime rank correlations or an analysis showing that judge scores differentiate creativity independently of coherence.
  2. [Abstract] Abstract and methods (implied by free parameters listed in axiom ledger): the exact mathematical definition of the per-token feature, the precise cumulative-mass thresholds used to compute n95(q) or related quantities, and whether those thresholds or any aggregation choices were selected or tuned on the same 500-prompt evaluation set are not stated. Without this information, it is impossible to assess whether the reported ρ values reflect a fixed, parameter-free derivation or post-hoc optimization that could inflate the gap over baselines.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major point below, agreeing where the manuscript requires clarification on scope and definitions. Revisions will be made to the abstract and methods to improve precision and interpretation.

read point-by-point responses

  1. Referee: [Abstract] Abstract, paragraph on mechanistic signature and regime separation: the reported correlations are driven exclusively by the T=1.5 incoherence regime (n95(q) rising from ~1 to ~131 tokens and 13pp mass leakage), while the per-token aggregates explicitly do not separate T=0.3 from T=0.8. Because the ground-truth rankings may largely encode coherence penalties rather than creativity distinctions within coherent outputs, the claim that the feature measures creativity (rather than incoherence detection) requires additional evidence, such as within-regime rank correlations or an analysis showing that judge scores differentiate creativity independently of coherence.

    Authors: We agree that the reported performance is driven by separation of the T=1.5 regime, consistent with the manuscript's explicit statement that per-token aggregates do not separate T=0.3 from T=0.8. The ground-truth rankings (both LLM and human) appropriately assign lower creativity to incoherent outputs, and the high inter-judge agreement (ρ=0.83) supports that this penalty reflects valid creativity assessment. We will revise the abstract to emphasize that the feature provides a strong signal via incoherence detection rather than within-coherent-regime distinctions. We will also add within-regime Spearman correlations and an analysis of judge scores versus independent coherence metrics to address the request for additional evidence. revision: yes

  2. Referee: [Abstract] Abstract and methods (implied by free parameters listed in axiom ledger): the exact mathematical definition of the per-token feature, the precise cumulative-mass thresholds used to compute n95(q) or related quantities, and whether those thresholds or any aggregation choices were selected or tuned on the same 500-prompt evaluation set are not stated. Without this information, it is impossible to assess whether the reported ρ values reflect a fixed, parameter-free derivation or post-hoc optimization that could inflate the gap over baselines.

    Authors: The full manuscript (Section 3, Methods) defines the per-token feature as the sequence-averaged difference in 95% cumulative-mass width n95(q) before versus after temperature scaling, where n95(q) is the minimal number of tokens whose probabilities sum to at least 95% of the total mass. The 95% threshold follows standard practice for effective support size and was fixed a priori without tuning on the 500-prompt set; no post-hoc optimization was performed. We will insert a concise version of this definition into the abstract and add an explicit statement in Methods confirming the parameter-free, pre-specified nature of all choices. revision: yes

Circularity Check

0 steps flagged

No circularity: feature derived independently from distributions and correlated with external judges

full rationale

The paper computes a per-token distributional feature (n95(q) width and mass leakage) directly from the model's token probabilities before and after temperature scaling on fixed prompts. This computation uses only the LLM's output logits at T in {0.3,0.8,1.5} and does not reference creativity labels, judge rankings, or any fitted parameters tied to the target variable. The reported Spearman correlations are post-hoc comparisons against independent external rankings (averaged GPT-4o/Gemini judges and human raters). No self-citations, ansatzes, or uniqueness theorems are invoked to justify the feature; the baselines are standard reference-free metrics evaluated on the same data. The derivation chain is therefore self-contained against external benchmarks and does not reduce to its inputs by construction.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard statistical correlation and the domain assumption that judge rankings measure creativity; the feature definition uses conventional percentile thresholds (95 percent, 90 percent) that are not fitted to the target labels.

free parameters (2)
  • cumulative-mass thresholds
    95 percent for n95(q) width and 90 percent for plausible set; chosen to quantify inflation and leakage.
  • temperature set
    Fixed values 0.3, 0.8, 1.5 selected for the experiment.
axioms (2)
  • standard math Spearman rho measures monotonic agreement between rankings
    Used to compare feature-derived ranks against judge ranks.
  • domain assumption LLM and human judge panels provide valid ground truth for creativity
    Central to interpreting the reported correlations as evidence for the feature.

pith-pipeline@v0.9.1-grok · 5941 in / 1523 out tokens · 34702 ms · 2026-06-28T17:05:03.304974+00:00 · methodology

discussion (0)

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Reference graph

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