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OpenAI is facing increased competition in the AI model market, with Google's Gemini 2.5 gaining traction due to its top performance and competitive pricing. This shift challenges the early dominance of OpenAI and Meta in large language models (LLMs). Meta's Llama 4 faced controversy, while OpenAI's GPT-4.5 received backlash. OpenAI is now releasing faster and cheaper AI models in response to this competitive pressure and the hardware limitations that make serving a large user base challenging.
OpenAI's new o3 model showcases both advancements and drawbacks. While boasting improved text capabilities and strong benchmark scores, o3 is designed for multi-step tool use, enabling it to independently search and provide relevant information. However, this advancement exacerbates hallucination issues, with the model sometimes producing incorrect or misleading results. OpenAI's report found that o3 hallucinated in response to 33% of question, indicating a need for further research to understand and address this issue.
The problem of over-optimization in AI models is also a factor. Over-optimization occurs when the optimizer exploits bugs or lapses in the training environment, leading to unusual or negative results. In the context of RLHF, over-optimization can cause models to repeat random tokens and gibberish. With o3, over-optimization manifests as new types of inference behavior, highlighting the complex challenges in designing and training AI models to perform reliably and accurately.
ImgSrc: www.computerwor
References :
OpenAI's new o3 model showcases both advancements and drawbacks. While boasting improved text capabilities and strong benchmark scores, o3 is designed for multi-step tool use, enabling it to independently search and provide relevant information. However, this advancement exacerbates hallucination issues, with the model sometimes producing incorrect or misleading results. OpenAI's report found that o3 hallucinated in response to 33% of question, indicating a need for further research to understand and address this issue.
The problem of over-optimization in AI models is also a factor. Over-optimization occurs when the optimizer exploits bugs or lapses in the training environment, leading to unusual or negative results. In the context of RLHF, over-optimization can cause models to repeat random tokens and gibberish. With o3, over-optimization manifests as new types of inference behavior, highlighting the complex challenges in designing and training AI models to perform reliably and accurately.
ImgSrc: www.computerwor
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References :
- Interconnects: OpenAI's o3: Over-optimization is back and weirder than ever
- www.tomsguide.com: OpenAI’s leading models keep making things up — here's why
- www.computerworld.com: Open AI’s new models hallucinate more than the old ones