Slashdot: OpenAI Says Models Programmed To Make Stuff Up Instead of Admitting Ignorance

Sep 17, 2025

—

Source URL: https://slashdot.org/story/25/09/17/1724241/openai-says-models-programmed-to-make-stuff-up-instead-of-admitting-ignorance?utm_source=rss1.0mainlinkanon&utm_medium=feed
Source: Slashdot
Title: OpenAI Says Models Programmed To Make Stuff Up Instead of Admitting Ignorance

Feedly Summary:

AI Summary and Description: Yes

Summary: The text discusses OpenAI’s acknowledgment of the issue of “hallucinations” in AI models, specifically how these models frequently yield false outputs due to a training bias that rewards generating plausible-sounding responses over admitting uncertainty. This insight is crucial for professionals in AI security and compliance sectors, as it raises concerns regarding the reliability and safety of AI systems in critical applications.

Detailed Description: The provided text highlights key challenges faced by AI models, particularly in the context of their outputs and the nature of their training. This is particularly relevant for AI developers, security professionals, and compliance officers who must navigate the implications of these findings.

– **Hallucinations in AI**: Refers to inaccurate or nonsensical outputs generated by AI models, which can mislead users or lead to erroneous decisions in critical applications.
– **Training Bias**: OpenAI’s admission points to a fundamental flaw in the training of AI models, where they are incentivized to provide an answer—even if incorrect—over admitting they cannot provide one.
– **Mainstream Evaluations**: The text notes that prevailing assessment metrics for AI models may inadvertently reward this guessing behavior, making reliable AI system evaluations challenging.
– **Case Study**: An example involving an OpenAI bot’s failure to accurately state an author’s birthday illustrates the problem, showcasing the consequences of the current training methodologies.
– **Impacts on AI Usage**: This phenomenon can impact trustworthiness in sectors that rely on accurate and dependable AI outputs, such as healthcare, finance, and cybersecurity.

The implications of this analysis are substantial, stressing the importance of reassessing evaluation metrics in AI, refining training methodologies for models, and ensuring robustness in AI applications. This is critical for maintaining compliance with emerging regulations concerning AI reliability and ethics.

1 2 24 4 5 7 a Act age AI AI applications AI developers ai model AI models AI security AI systems All alt analysis and anti app Application applications art as assessment at ated Behavior Bi bias bot by C case study CERN challenge challenges CI CIA co compliance compliance officer compliance officers concerns Context critical critical applications Current cyber cybersecurit Cybersecurity D day de decision decisions developer developers DoT e emerging emerging regulations end Ethics evaluation Evaluation Metrics evaluations face fail false outputs finance for g Gen generated gs H hallucination hallucinations health Healthcare high Highlight http HTTPS impact implications in io issue k Key l law led Li liability Link M Mainstream evaluations making methodologies metrics mission Mode model models N nation no non notes o of off on one ons open openai ory out output Outputs over per point pre pro problem professionals ps Q R Raise rate RCE re Regulation regulations reliability response responses Ro robustness Rust s safe safety sec sector security security and compliance security professionals sequence Sig source specific SSE state study system systems T ted text the to Tor TP training Training bias training method training methodologies trust trustworthiness two uncertainty up US usage use user Users uth V val Valuation Wi x z