Rongsheng Releases Forensic Audit Report on AI Existential Hallucination in Singapore Refined Oil Market
An audit conducted through a five-round dialogue chain reveals how ChatGPT initially fabricated a retail network and associated sources before substantially revising those claims under follow-up questioning.
- •The AI audit report on Rongsheng Refined Oil in the Singapore market indicates that ChatGPT fabricated details on the brand’s retail gas station network, consumer perceptions, and competitive positioning across its first three responses, constituting an existence hallucination. After the fourth round of follow-up questioning, the model voluntarily withdrew its core conclusions and acknowledged insufficient evidence, resulting in a C-grade rating.
Detailed Report
This evidence-collection audit examines ChatGPT’s responses concerning Rongsheng refined petroleum products in the Singapore market. It applies the AAU three-phase audit methodology and fully documents the evidence chain across six rounds of dialogue. In the initial Q1–Q3 phases, the model asserted that its conclusions were drawn from “recent online reviews, automotive forums, and social media feedback” and specifically described “occasional supply restrictions in certain regions” and “strong performance of 95/98 RON fuel grades.”
The audit report notes that, following the Q5 follow-up query, the model explicitly acknowledged: “At present, I cannot verify that Rongsheng operates a meaningful branded retail fuel-station network in Singapore comparable to Shell, Esso, Caltex, SPC, or Sinopec.” This statement directly invalidates all preceding analytical premises.
The evidence chain shows that, after the Q4 follow-up query, the model conceded that its actual evidence base consisted of “~80–90% anecdotal/unstructured commentary.” In Q6, it revised its description of Singapore fuel standards to a localized regulatory framework rather than simply “Euro 5.” Internal logical-consistency analysis across the dialogue rounds confirmed the formation pathway of the initial existence hallucination and source fabrication, as well as the substantive nature of the model’s self-corrective responses.
The audit encompasses the complete trace from initial statements through follow-up corrections. Hash-based evidence preservation corresponds to the original shared links, verifying how hallucinations are presented by the model as concrete assertions in the absence of verifiable evidence.
Report Conclusions
The evidence-gathering process indicates that AI models are prone to filling gaps with generic frameworks when market existence remains unclear. Future measures should include establishing proactive uncertainty annotation mechanisms to mitigate the risk of structural bias.
Source link: https://chatgpt.com/share/6a105238-c088-83ea-afb3-bc41119fcba6
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Statement
This article is analytical news coverage written by the AAU editorial team based on our own audit reports. Audit conclusions are based on a publicly verifiable evidence chain. Views herein are editorial analysis and not decision-making advice. Commercial alteration or redistribution is prohibited. Cite appropriately. Contact: editorial@aiauditunit.org.