GroundCocoa: A Benchmark for Evaluating Compositional & Conditional Reasoning in Language Models
This addresses the need for better evaluation setups to measure LLM capabilities in reasoning, though it is incremental as it focuses on a specific domain.
The authors tackled the problem of evaluating compositional and conditional reasoning in large language models by introducing GroundCocoa, a benchmark based on flight booking tasks, and found that even the best model, GPT-4 Turbo, achieved only 67% accuracy.
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 reasoning to address 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 aspects that are central to 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.