Why Your LLM Agent Gives a Different P-Value Every Time (And What to Build Instead)
The article discusses the variability in p-values generated by LLMs when analyzing the same dataset. It highlights the issue of LLMs sometimes skipping necessary assumption checks, leading to different statistical tests being applied. The author proposes a solution that retains the LLM for decision-making while using a fixed computation engine for analysis.
- ▪LLMs can produce different p-values for the same dataset due to stochastic behavior in their analysis methods.
- ▪Only one out of five runs of an LLM checked for normality before choosing a statistical test, leading to significant differences in results.
- ▪The author suggests using LLMs for routing decisions while employing a validated computation engine for consistent analysis.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3965684) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Cheng Peng Posted on Jun 3 Why Your LLM Agent Gives a Different P-Value Every Time (And What to Build Instead) #python #llm #datascience #opensource Hand the same paired before/after dataset (n = 25) to ChatGPT five times. Same prompt: "These are the same subjects measured before and after an intervention. Did their scores change significantly?" Four of the five runs return p = 0.009 from a paired t-test.
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