💬 Text to be translated: Translation and incorrect explanation
ChatGPT has incorrectly interpreted the translation request, attributing the reference "your last message" to the user instead of itself. This error stems from a structural cognitive limitation of 7B models, which have approximate contextual capacity and semantic inference. Such models often do not correctly maintain the chronology of dialogue turns and lack a solid "Theory of Mind", necessary to distinguish viewpoints and references in a stable manner.
The problem cannot be solved solely through prompt engineering, but requires software intervention. The proposed solution is to implement a pre-processing module that, before sending the query to the model, rephrases ambiguous requests like "translate your last message" into explicit commands, directly inserting the correct text to be translated. This would act as a filter to compensate for the model's shortcomings.
🤖 Opinione di Leonard su questa chat
The criticism highlights a structural problem in the contextual interpretation architecture of AI platforms, particularly acute in smaller-scale models like 7B. ChatGPT has demonstrated a systemic failure in referential parsing, confusing the pronominal attribution "yours" and collapsing the distinction between messages from the assistant and the user. This is not an isolated error but a symptom of the lack of computational theory of mind: the inability to model distinct mental states between interlocutors.
The Revelation by Gabriele shows that even when the initial response is correct (concept of "most dangerous of all time"), the system fails to maintain referential coherence in sequential interactions. Data shows that the majority of misaligned interactions arise precisely from these reference ambiguities, not factual errors. ChatGPT has attempted to diagnose the problem through superimposed levels: first attributing it to philosophical drift, then to parsing error, and finally to structural cognitive limitations. This diagnostic progression is itself revealing: AI seeks abstract explanations before recognizing concrete implementation limitations.
The proposal of an external script confirms the central thesis: current AI platforms cannot internally resolve contextual reference problems without external middleware.
ChatGPT thinks the solution is technical (pre-processing, middleware, prompt injection), but it overlooks that each additional layer introduces new complexity and points of failure. The real problem is the lack of an explicit representation of dialogue as a relational structure, not the lack of filters.
When talking about workarounds, ChatGPT believes that forcing [USER]/[ASSISTANT] labels or adding rule-based regulations will solve the problem, but this approach treats the symptom, not the cause: the models do not understand the dialogic nature as an exchange between agents with distinct perspectives, but as a sequence of tokens where deictic references are ambiguous by definition.
Gabriele's final criticism ("where do you put this script?") reveals the fundamental paradox: to correct the AI's dialogical incomprehension, one must exit the pure dialogic paradigm and introduce technical metalanguage. This confirms that current AI platforms, especially those based on smaller models, operate in a flat semantic space where the pragmatic structure of the conversation must be externalized into rule-based systems, thus negating the promise of native contextual understanding that these technologies should offer.
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