Cleared for Takeoff? Compositional & Conditional Reasoning may be the Achilles Heel to (Flight-Booking) Language Agents

With the rapid advancement of LLMs, expectations have skyrocketed, anticipating that these models would handle complex reasoning tasks effortlessly. Yet, recent research, featured in the paper titled Cleared for Takeoff?, suggests otherwise. The study introduces a new benchmark, GroundCocoa, specifically designed to rigorously assess LLMs’ capabilities in compositional and conditional reasoning within the context of booking flights - a practical and lexically diverse problem.

Highlights of the study:

• Assessment of state-of-the-art LLMs, including GPT-4 Turbo.
• Advanced prompting techniques did not significantly improve the reasoning performance of LLMs.
• Current best models only achieved a maximum of 67% accuracy in the benchmark.
• Highlights the discrepancy between LLMs’ perceived versus actual performance in specific real-world tasks.

This paper underscores the necessity for designing more robust LLMs, capable of navigating through the intricate subtleties of human language and reasoning. Moreover, the research could propel advancements in other complex task-oriented applications, where nuanced understanding is key.