AI misinformation
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Issues with GPT-5.2 Auto/Instant in ChatGPT
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The GPT-5.2 auto/instant mode in ChatGPT is criticized for generating responses that can be misleading, as it often hallucinates and confidently provides incorrect information. This behavior can tarnish the reputation of the GPT-5.2 thinking (extended) mode, which is praised for its reliability and usefulness, particularly for non-coding tasks. Users are advised to be cautious when relying on the auto/instant mode to ensure they receive accurate and trustworthy information. Ensuring the accuracy of AI-generated information is crucial for maintaining trust and reliability in AI systems.
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Local LLMs and Extreme News: Reality vs Hoax
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The experience of using local language models (LLMs) to verify an extreme news event, such as the US attacking Venezuela and capturing its leaders, highlights the challenges faced by AI in distinguishing between reality and misinformation. Despite accessing credible sources like Reuters and the New York Times, the Qwen Research model initially classified the event as a hoax due to its perceived improbability. This situation underscores the limitations of smaller LLMs in processing real-time, extreme events and the importance of implementing rules like Evidence Authority and Hoax Classification to improve their reliability. Testing with larger models like GPT-OSS:120B showed improved skepticism and verification processes, indicating the potential for more accurate handling of breaking news in advanced systems. Why this matters: Understanding the limitations of AI in processing real-time events is crucial for improving their reliability and ensuring accurate information dissemination.
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ChatGPT’s Inconsistency on Charlie Kirk’s Status
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An example highlights the limitations of large language models (LLMs) like ChatGPT, which initially dismissed a claim about Charlie Kirk's death as a conspiracy theory, then verified and acknowledged the claim before reverting to its original stance. This inconsistency underscores the gap between the perceived intelligence of LLMs and their actual reliability, as they can confidently provide contradictory information. The incident serves as a reminder that while LLMs often appear intelligent, they are not infallible and can make errors in information processing. Understanding the strengths and weaknesses of AI is crucial as reliance on such technology increases.