Unverified50% confidenceOpinionExact time
The biggest risk of large models in data analysis scenarios is fabricating seemingly plausible but actually incorrect data when the corresponding field cannot be found.
1
Sources
50%
Confidence
Long-term
Relevance
9/19/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe greatest risk of using synthetic data lies in the potential lack of real-world relevance, seasonality, noise, and outliers, which can distort insights.77% similarUnverified错误的归纳偏置会让模型花费容量拟合数据中不存在的虚假模式,导致分布外数据上严重失效77% similarUnverifiedLarge models do not guarantee that every statement is correct, every piece of data is up to date, or every step of reasoning is error-free, and important information requires cross-verification.76% similarVerifiedLarge language models have hallucination problems and may fabricate non-existent references, invent experimental data, or provide incorrect statistical figures.76% similarUnverifiedLLM幻觉问题源于模型基于概率预测生成文本而非检索真实数据,在涉及具体统计数字、引文来源和法律条款时尤为危险74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/935187API
curl https://kongchang.com/api/v1/knowledge/claims/935187MCP
get_claim(id=935187)