Cleared for Takeoff? Compositional & Conditional Reasoning may be the
  Achilles Heel to (Flight-Booking) Language Agents
Harsh Kohli Huan Sun
The rapid progress of large language models (LLMs) has seen them excel and
frequently surpass human performance on standard benchmarks. This has enabled
many downstream applications, such as LLM agents, to rely on their
sophisticated reasoning to navigate complex task requirements. However, LLMs
are known to unexpectedly falter in simple tasks and under seemingly
straightforward circumstances - underscoring the need for better and more
diverse evaluation setups to measure their true capabilities. To this end, we
choose to study compositional and conditional reasoning, two cornerstones of
human cognition, and introduce GroundCocoa - a lexically diverse benchmark
connecting these reasoning skills to the real-world problem of flight booking.
Our task involves aligning detailed user preferences with available flight
options presented in a multiple-choice format. Results indicate a significant
disparity in performance among current state-of-the-art LLMs with even the best
performing model, GPT-4 Turbo, not exceeding 67% accuracy despite advanced
prompting techniques.