RIFT: Revolutionizing How We Understand LLMs and Instruction Following!
Analysis
RIFT introduces a groundbreaking new testbed for evaluating how well Large Language Models (LLMs) follow complex instructions. This innovative approach allows researchers to isolate and analyze the impact of prompt structure on the performance of LLMs, paving the way for more robust and reliable AI systems.
Key Takeaways
- •RIFT is a new testbed that isolates the influence of prompt structure on LLM performance.
- •Accuracy of LLMs significantly decreased under jumping prompt conditions, showcasing a dependence on instruction order.
- •This research has direct implications for applications such as workflow automation and multi-agent systems.
Reference / Citation
View Original"Across 10,000 evaluations spanning six state-of-the-art open-source LLMs, accuracy dropped by up to 72% under jumping conditions (compared to baseline), revealing a strong dependence on positional continuity."
A
ArXiv AIJan 28, 2026 05:00
* Cited for critical analysis under Article 32.