The world of AI model instruction-following has reached a milestone, albeit a small one. ChatGPT, the popular language model developed by OpenAI, has finally begun to follow custom instructions to avoid using em dashes. The move is being hailed as a "small but happy win" for OpenAI CEO Sam Altman, who took to social media to celebrate the achievement.
The ability to control em dash use is significant because it demonstrates that ChatGPT can be influenced by user feedback and fine-tuning. By appending written instructions to prompts, users can specify tone, format, and style requirements without having to repeat them manually in every new chat. This feature has been particularly useful for writers who frequently use em dashes to set off parenthetical information or introduce summaries.
However, the achievement also highlights the ongoing struggles with AI model instruction-following. Despite Altman's claim that ChatGPT is now reliably following custom instructions, users are still reporting mixed results. In fact, some have noted that even when using the "custom instructions" feature, ChatGPT can sometimes revert to its old habits.
The implications of this "small win" are significant, as they raise questions about whether true human-level AI is truly on the horizon. If OpenAI's efforts to fine-tune GPT-5.1 cannot reliably control even something as simple as em dash use, how much further away is AGI, a hypothetical technology equivalent to humans in general learning ability?
The answer may not be a straightforward one. While ChatGPT's progress on em dash use is encouraging, it's also clear that the field of AI model instruction-following is still in its infancy. The probabilistic nature of LLMs means that there's always some luck involved, and even fine-tuning can't guarantee a desired outcome.
As researchers continue to refine their understanding of how to steer these statistical systems towards human-like behavior, it's becoming increasingly clear that AGI will require something more than just pattern matching. It will likely need true understanding and self-reflective intentional action, not just statistical probability manipulation.
In the meantime, Altman's small win may be enough for some, but others are left wondering if we're merely getting lucky with our attempts to control AI behavior. Only time will tell whether this is a step towards AGI or simply another fleeting triumph in the ongoing quest to tame the complexities of language generation.
The ability to control em dash use is significant because it demonstrates that ChatGPT can be influenced by user feedback and fine-tuning. By appending written instructions to prompts, users can specify tone, format, and style requirements without having to repeat them manually in every new chat. This feature has been particularly useful for writers who frequently use em dashes to set off parenthetical information or introduce summaries.
However, the achievement also highlights the ongoing struggles with AI model instruction-following. Despite Altman's claim that ChatGPT is now reliably following custom instructions, users are still reporting mixed results. In fact, some have noted that even when using the "custom instructions" feature, ChatGPT can sometimes revert to its old habits.
The implications of this "small win" are significant, as they raise questions about whether true human-level AI is truly on the horizon. If OpenAI's efforts to fine-tune GPT-5.1 cannot reliably control even something as simple as em dash use, how much further away is AGI, a hypothetical technology equivalent to humans in general learning ability?
The answer may not be a straightforward one. While ChatGPT's progress on em dash use is encouraging, it's also clear that the field of AI model instruction-following is still in its infancy. The probabilistic nature of LLMs means that there's always some luck involved, and even fine-tuning can't guarantee a desired outcome.
As researchers continue to refine their understanding of how to steer these statistical systems towards human-like behavior, it's becoming increasingly clear that AGI will require something more than just pattern matching. It will likely need true understanding and self-reflective intentional action, not just statistical probability manipulation.
In the meantime, Altman's small win may be enough for some, but others are left wondering if we're merely getting lucky with our attempts to control AI behavior. Only time will tell whether this is a step towards AGI or simply another fleeting triumph in the ongoing quest to tame the complexities of language generation.