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Scientists have raised concerns about the suitability of large language models (LLMs) for real-world applications. They warn that even minor changes can lead to significant failures in these models, causing their world models to collapse. This has raised questions about the reliability and effectiveness of LLMs in practical use. Researchers are exploring new ways to mitigate these issues, including pitting LLMs against each other in debates. Additionally, the benchmarks used to evaluate and train these models are often flawed, further complicating their deployment in real-world scenarios. These concerns are particularly relevant in the field of Artificial Intelligence and Natural Language Processing (NLP).
The consensus among serious researchers, engineers & practitioners was always that scale is not enough. Training ever larger statistical models was never going to lead to intelligence in machines. https://t.co/37zkv1h6iN
Scaling is not enough to reach AGI A new architecture is important to generalize new tasks, without it, AGI remains far away next year. The only good thing is increased funding for AI research, but achieving AGI requires an entirely new system.
Common way to test for leaks in large language models may be flawed https://t.co/T5k9i4thHO #AIResearch #MachineLearning #LanguageModels #ArtificialIntelligence #DataScience #ModelTesting #AIFlaws #TechDiscussion #ResearchInsights #ComputationalLinguistics https://t.co/xHEJo0xUnb
