Estimating the Self-Consistency of LLMs
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911173030445056 |
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| author | Nowak, Robert |
| author_facet | Nowak, Robert |
| contents | Systems often repeat the same prompt to large language models (LLMs) and aggregate responses to improve reliability. This short note analyzes an estimator of the self-consistency of LLMs and the tradeoffs it induces under a fixed compute budget $B=mn$, where $m$ is the number of prompts sampled from the task distribution and $n$ is the number of repeated LLM calls per prompt; the resulting analysis favors a rough split $m,n\propto\sqrt{B}$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19489 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimating the Self-Consistency of LLMs Nowak, Robert Artificial Intelligence Systems often repeat the same prompt to large language models (LLMs) and aggregate responses to improve reliability. This short note analyzes an estimator of the self-consistency of LLMs and the tradeoffs it induces under a fixed compute budget $B=mn$, where $m$ is the number of prompts sampled from the task distribution and $n$ is the number of repeated LLM calls per prompt; the resulting analysis favors a rough split $m,n\propto\sqrt{B}$. |
| title | Estimating the Self-Consistency of LLMs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.19489 |