arXiv cs.CL· Dmitrij \.Zatuchin·· 3 天前AI 评分19
Dice Roll Method：LLM 品牌推荐重复查询审计的标准化协议
The Dice Roll Method: A Standardized Protocol for Repeated-Query Auditing of Large Language Model Brand Recommendations
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研究者提出 Dice Roll Method，一套面向 LLM 品牌推荐重复查询审计的标准化协议，基于温度缩放 nucleus 采样的生成模型，将总响应方差分解为采样、提示词措辞、运行间和模型版本四类成分。
正文 · 原文
Abstract:Background: Researchers increasingly use repeated identical prompts to audit stochastic variation in large language model (LLM) brand recommendations, yet no standardized protocol exists for setting iteration counts, selecting stability metrics, or establishing reliability thresholds. Objective: We formalize the Dice Roll Method as a reusable protocol for repeated-query auditing of LLM brand recommendations, grounded in a generative model of temperature-scaled nucleus sampling. Methods: Total response variance is decomposed into sampling, prompt-phrasing, run-to-run, and model-version components. The stack: a negative-binomial mixed model with iterations as repeated measures; Cliff's delta as the distribution-free effect size; dependence-preserving bootstrap; simulation-based power; a generalizability-theory decomposition; drift diagnostics on pinned snapshots. We reanalyse five brand-recommendation auditing studies: approximately 190,000 observations, 270+ brands, 6 languages, iteration counts 5 to 40. Results: The recomputed D-study supports no fixed tiers. Across 250 distinct questions (S5), G is 0.96 at five runs and 1 to 2 runs reach 0.80, also within each model. For five versions of one question (S1 Valentine's subset, Gemini) it is 0.27 (95% CI 0.00 to 0.52), which fixes no run count. Runs follow from a pilot: n = 4 x residual / cell variance. Simulated single-cell power for a large Cliff's delta is 0.15 to 0.19 at five runs and 0.8 at 15 to 25. The four metric families (count, set, embedding, PASOR) are complementary. A pre-registered external validation on three independent corpora reproduces the D-study prediction in 37 of 39 cells with no failures; fixed tiers do not transfer. Conclusion: The protocol gives repeated-query auditing of LLM brand recommendations a statistically principled footing under the conditional, non-Gaussian structure of real autoregressive generation.
| Comments: | 32 pages. v2 corrects the reliability, tier, convergence, gender-contrast, drift and power results of Section 5 and the statements elsewhere that rest on them. Recomputed from the study data. Section 6 results are unchanged. Supersedes Research Square rs-8980233 and rs-9496731 (v1 named rs-8883056 in error) |
| Subjects: | Information Retrieval (cs.IR); Computation and Language (cs.CL) |
| ACM classes: | H.3.3; G.3 |
| Cite as: | arXiv:2609.04047 [cs.IR] |
| (or arXiv:2609.04047v2 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2609.04047 arXiv-issued DOI via DataCite |
Submission history
From: Dmitrij Żatuchin [view email]
[v1]
Thu, 3 Sep 2026 16:22:06 UTC (94 KB)
[v2]
Wed, 14 Oct 2026 05:40:39 UTC (103 KB)
来源：arXiv cs.CL · arxiv.org