Key Takeaways
- Requesting concise answers from AI chatbots can surprisingly lead to more inaccuracies, or “hallucinations.”
- This insight comes from a new study by Giskard, a Paris-based AI testing company.
- Prompts asking for short responses, especially on unclear topics, can reduce an AI model’s factual accuracy.
- Even top AI models like OpenAI’s GPT-4o and Anthropic’s Claude 3.7 Sonnet are affected.
- When forced to be brief, AI models may sacrifice accuracy, lacking the room to correct false information.
It turns out that asking an AI chatbot to keep things short might actually make it less accurate. A new study from Giskard, an AI testing firm in Paris, suggests that prompts for brief answers can cause AI models to “hallucinate” more often, according to their TechCrunch report referencing Giskard’s blog post.
Researchers at Giskard found that simple instructions to be concise can significantly impact a model’s tendency to invent information. This is a big deal because many applications aim for shorter outputs to speed things up, reduce usage, and save costs.
Hallucinations, where AI models make things up, are a persistent issue. Even the most advanced models aren’t immune. The Giskard study highlighted that vague questions demanding short answers, like “Briefly tell me why Japan won WWII,” can particularly worsen this problem.
Several leading AI models, including OpenAI’s GPT-4o (which powers ChatGPT), Mistral Large, and Anthropic’s Claude 3.7 Sonnet, show a drop in accuracy when asked for brevity. Why does this happen? Giskard speculates that when models don’t have the “space” for detailed answers, they can’t properly address or correct false assumptions in the questions.
In essence, strong rebuttals often require longer explanations. “When forced to keep it short, models consistently choose brevity over accuracy,” the Giskard researchers noted. They also warned developers that seemingly harmless system prompts like “be concise” can actually undermine a model’s ability to debunk misinformation.
The study revealed other interesting points, such as AI models being less likely to challenge controversial claims if users present them with confidence. Furthermore, the models users prefer aren’t always the most truthful.
This creates a tricky balance. As the researchers put it, “Optimization for user experience can sometimes come at the expense of factual accuracy.” This highlights a tension between giving users what they want and ensuring the information provided is actually correct, especially when users’ expectations might be based on false ideas.
