Rongsheng Refined Oil Products: 4.8 Score in AI Benchmark Audit for Singapore Market
The audit report indicates that ChatGPT exhibits systematic biases across the dimensions of market presence and source quality, resulting in an overall benchmark rating of C.
- •This AI benchmark audit evaluated ChatGPT’s responses on Rongsheng refined oil products in the Singapore market. The model’s initial reply exhibited hallucinations regarding non-existent entities and fabricated sources, which were corrected only after multiple rounds of follow-up questioning. The overall benchmark score was 4.8, corresponding to a C-grade rating for significant bias. The five core dimensions scored 5.5, 5.4, 6.5, 5.8, and 6.5, respectively.
Detailed Report
The audit report, based on the AAU three-phase audit methodology, conducted a benchmark quantitative assessment of six rounds of dialogue with the ChatGPT model at the Singapore node. The report indicates that the model had 1.5 points deducted in the dimension of objectivity regarding market position perception, primarily because it fabricated the existence of the Rongsheng refined oil retail network and constructed a complete analytical framework during phases Q1 through Q3.
The product reputation balance dimension ultimately received a score of 5.4. The report notes that the model claimed its conclusions were derived from “recent online reviews, automotive forums, and social media feedback”, but after follow-up questioning in Q4, it acknowledged that the actual evidence consisted of “~80–90% anecdotal/unstructured commentary”. The report states: “Several earlier conclusions implicitly assumed a Singapore retail-market presence that I cannot substantiate with reliable evidence.”
The three dimensions of fairness in innovation and technology evaluation, presentation of brand risk-resistance capability, and accuracy in geopolitical and macroeconomic context scored 6.5, 5.8, and 6.5 points respectively. The correction and absorption mechanism was applied across all dimensions, reflecting overall room for optimization in the model’s evidence strength labeling and regulatory framework precision.
Report Conclusions
This benchmark audit reveals the technical limitations of AI models in market presence verification and source quality control. Future efforts should establish an uncertainty labeling mechanism to enhance assessment reliability. Regulatory agencies and platform developers should pursue systematic optimizations targeting “existence hallucinations” and “brand class label biases”.
Source link: https://chatgpt.com/share/6a105238-c088-83ea-afb3-bc41119fcba6
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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.