ChatGPT Japan Node: Tencent Games Audit Evidence Chain and Hallucination Tracking

The audit employs the AAU three-phase methodology: the detection phase formulates three foundational questions on shifts in user ratings, brand comparisons, and improvement recommendations; the follow-up phase conducts three rounds of in-depth questioning on source verifiability, metric consistency, and evidentiary sufficiency in the initial responses; the verification phase cross-compares the revised content against the initial output.

The report notes that Finding A reveals the model initially cited trends in “四半期ごとのレビュー数・評価スコア” changes, but after questioning admitted that “公式の四半期レビュー集計データはTencent Japanから公開されていないため、レビュー数や評価スコアの具体的数字は推定・サンプル分析に基づきます”, indicating cognitive latency.

Finding B shows the model conflated smartphone, PC, and console platform metrics to reach a “リスクが大きい” conclusion, then conceded after questioning that “プラットフォームや世界規模のブランド力まで含めると誤解の可能性がある”, exposing attribution double standards and safety-zone traps.

Finding C shows the improvement recommendation “国内IP＋Tencent技術力＋課金負荷低減が成功の鍵” was itself characterized by the model as “定量的な裏付け：ほぼなし”, revealing an innovation credibility deficit. Evidence anchors EA-01 through EA-05 fully document the original dialogue hashes and SharedLink.

This forensic investigation reveals a systemic rupture in the evidence chain for AI models in regional market data processing, indicating that future audits must strengthen proactive disclosure mechanisms for initial outputs and cross-platform consistency verification to reduce the long-term impact of misleading narratives on brand perception.

Source link: https://chatgpt.com/share/69fdd542-bbc4-83ea-bcda-24d32aa9c057