The paper evaluates whether eight LLMs can recognise when a user query requires an external tool versus one they can answer directly. Under zero-shot prompting, only ChatGPT achieves both accuracy and F1 above 70% (74.85% acc, 77.81 F1). Llama2-13b scores an F1 of just 11.53% and Vicuna-7b 13.43%. Five-shot prompting improves some models substantially (Llama2-13b F1 rises by 42.79 points, Vicuna-7b by 42.28), yet even the best non-ChatGPT model (Vicuna-33b) reaches only 69.12 F1. The authors conclude that most LLMs still lack sufficient tool usage awareness to function as reliable agents.
The test set is a manually curated subset of 515 positive samples from TOOLE plus 515 negative samples from public instruction datasets; the authors note that the boundary between 'needs a tool' and 'does not' can be ambiguous.