“Generally, if a user makes a request of a GenAI tool, they desire an output that appropriately addresses the prompt (that is, a correct answer to a question). However, sometimes AI algorithms produce outputs that are not based on training data, are incorrectly decoded by the transformer or do not follow any identifiable pattern. In other words, it ‘hallucinates’ the response. The term may seem paradoxical, given that hallucinations are typically associated with human or animal brains, not machines. But from a metaphorical standpoint, hallucination accurately describes these outputs, especially in the case of image and pattern recognition (where outputs can be truly surreal in appearance).”

“However, AI hallucinations present a problem when it comes to delivering accurate and correct information, especially if users take the information at face value without any checks or oversight. ‘This is especially problematic in domains where decisions depend on factual precision, like medicine, law or finance,’ Watson said. ‘While more advanced models may reduce the frequency of obvious factual mistakes, the issue persists in more subtle forms. Over time, confabulation erodes the perception of AI systems as trustworthy instruments and can produce material harms when unverified content is acted upon.” And this problem looks to be exacerbated as AI advances. “As model capabilities improve, errors often become less overt but more difficult to detect.’”

“RAG allows GenAI models to access additional external knowledge bases, such as internal organizational data, scholarly journals and specialized datasets. By integrating relevant information into the generation process, chatbots and other natural language processing (NLP) tools can create more accurate domain-specific content without needing further training.”

Key Findings from the Paper

Systemic Problem: Hallucinations are not simply bugs but a systemic consequence of how AI models are trained and evaluated.

Evaluation Incentives: Standard evaluation methods, particularly binary grading systems, reward models for generating an answer, even if it’s incorrect, and punish them for admitting uncertainty.

Pressure to Guess: This creates a statistical pressure for large language models (LLMs) to guess rather than say “I don’t know,” as guessing can improve test scores even with the risk of being wrong.

Fundamental Flaw: The paper highlights a fundamental flaw in the industry’s approach to evaluating AI progress, prioritizing raw accuracy over reliability and factual correctness.

“I’d need more context to answer this question accurately. There have been many baseball games with rain delays on Wednesday nights in Cleveland over the years, and players’-only meetings during such delays aren’t always widely reported or memorable events.

Could you provide more details about:

• What year or approximate timeframe this occurred?
• Which team(s) were involved?
• Any other context about the situation or why this meeting was significant? With more specific information, I’d be better able to help identify who called the meeting and the circumstances surrounding it.”

Jason Heyward Rallied Cubs with Players’-Only Meeting During Historic World Series Rain Delay

During a tense rain delay in the decisive Game 7 of the 2016 World Series, Chicago Cubs right fielder Jason Heyward called a players’-only meeting that has since become a legendary moment in the franchise’s storied history. The brief stoppage in play occurred on a Wednesday night in Cleveland after the ninth inning, with the Cubs and the then–Cleveland Indians locked in a tie. With the weight of a 108-year championship drought on their shoulders, Heyward gathered his teammates in the weight room behind the visitor’s [sic] dugout at Progressive Field … Following the delay and Heyward’s impromptu meeting, the Cubs came out in the top of the tenth inning and scored two runs. The Indians would rally for one run in the bottom of the frame, but the Cubs ultimately held on to win 8-7, securing their first World Series title since 1908.