Unverified50% confidenceCautionExact time
In data pipeline debugging scenarios, LLM hallucinations directly manifest as incorrect root cause identification, potentially misleading engineers in their troubleshooting.
1
Sources
50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedHallucination problems in LLM code generation manifest as fabricating non-existent APIs, incorrectly combining parameter orders of real APIs, or generating deprecated usage based on outdated documentation.74% similarUnverifiedLLM幻觉问题源于模型基于概率预测生成文本而非检索真实数据，在涉及具体统计数字、引文来源和法律条款时尤为危险72% similarUnverifiedIn multi-agent systems, error propagation manifests as fault cascading; because LLMs tend to over-trust inputs (sycophancy), errors that enter the information flow are difficult for subsequent steps to correct on their own.70% similarUnverifiedRetrofitting a disorganized data model with an NLQ interface often produces a large number of hallucinated incorrect answers, undermining business trust in the data platform.69% similarUnverified由检索驱动的幻觉通常不会触发异常或错误码，系统技术层面看正常运行，只有人工核验语义内容才能发现问题69% similar
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