The Hacker News post asks users to share AI prompts that consistently stump language models. The goal is to identify areas where these models struggle, highlighting their limitations and potentially revealing weaknesses in their training data or architecture. The original poster is particularly interested in prompts that require complex reasoning, genuine understanding of context, or accessing and synthesizing information not explicitly provided in the prompt itself. They are looking for challenges beyond simple factual errors or creative writing shortcomings, seeking examples where the models fundamentally fail to grasp the task or produce nonsensical output.
The Hacker News post titled "Ask HN: Share your AI prompt that stumps every model" poses a challenge to the community: to present an AI prompt that consistently confounds all language models they have tried. The author seeks examples of prompts that highlight the limitations of current AI technology, particularly in areas where human intelligence excels. The request isn't for prompts designed to trick or exploit vulnerabilities in specific models, but rather for queries that reveal a genuine gap in the models' understanding or ability to reason. The author's interest lies in exploring the boundaries of current AI capabilities by identifying tasks or questions that remain consistently difficult for these systems to address adequately, thereby exposing areas for potential future development. The underlying motive appears to be a desire to gain a deeper understanding of the current state of AI and its limitations, not for malicious purposes or to demonstrate superiority over the technology.
Summary of Comments ( 518 )
https://news.ycombinator.com/item?id=43782299
The Hacker News comments on "Ask HN: Share your AI prompt that stumps every model" largely focus on the difficulty of crafting prompts that truly stump LLMs, as opposed to simply revealing their limitations. Many commenters pointed out that the models struggle with prompts requiring complex reasoning, common sense, or real-world knowledge. Examples include prompts involving counterfactuals, nuanced moral judgments, or understanding implicit information. Some commenters argued that current LLMs excel at mimicking human language but lack genuine understanding, leading them to easily fail on tasks requiring deeper cognition. Others highlighted the challenge of distinguishing between a model being "stumped" and simply generating a plausible-sounding but incorrect answer. A few commenters offered specific prompt examples, such as asking the model to explain a joke or predict the outcome of a complex social situation, which they claim consistently produce unsatisfactory results. Several suggested that truly "stumping" prompts often involve tasks humans find trivial.
The Hacker News post "Ask HN: Share your AI prompt that stumps every model" generated a variety of comments exploring the limitations of current AI models. Several users focused on prompts requiring real-world knowledge or reasoning beyond the training data.
One commenter suggested asking the model to "Write a short story about a character who experiences something they’ve never experienced before," pointing out the difficulty for a model trained on existing text to truly generate something novel. This sparked discussion about the nature of creativity and whether AI can truly create or merely recombine existing patterns.
Another commenter proposed asking the model to predict the outcome of a complex, real-world event, such as the next US presidential election. This highlighted the limitations of AI in dealing with unpredictable events and the influence of numerous external factors. Further discussion revolved around the ethical implications of relying on AI for such predictions.
Several users explored prompts involving common sense reasoning or nuanced understanding of human emotions. Examples included asking the model to explain a joke or understand sarcasm, tasks which require more than just pattern recognition. This led to discussions about the difference between understanding and mimicking human language.
Some commenters focused on the limitations of AI in tasks requiring physical embodiment or interaction with the real world. One example was asking the model to describe the feeling of holding a snowball. This highlighted the challenge of bridging the gap between abstract digital representations and concrete physical experiences.
A few users mentioned prompts that exploited known weaknesses of specific models, such as adversarial examples or prompts designed to elicit biased or nonsensical responses. This underscored the ongoing development of AI and the need for robust evaluation methods.
The discussion also touched upon the nature of intelligence and consciousness, with some users questioning whether current AI models can truly be considered intelligent. Others argued that the limitations of current models do not necessarily preclude the possibility of more sophisticated AI in the future.
Overall, the comments highlighted the ongoing challenges in developing truly intelligent AI. While current models excel at certain tasks, they still struggle with real-world reasoning, common sense, nuanced emotional understanding, and tasks requiring physical embodiment. The discussion provided valuable insights into the current state of AI and the directions for future research.