$\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
<|reference_start|>$\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains: Existing benchmarks do not test language agents on their interaction with human users or ability to follow domain-specific rules, both of which are vital for deploying them in real world applications. We propose $\tau$-bench, a benchmark emulating dynamic conversations between a user (simulated by language models) and a language agent provided with domain-specific API tools and policy guidelines. We employ an efficient and faithful evaluation process that compares the database state at the end of a conversation with the annotated goal state. We also propose a new metric (pass^k) to evaluate the reliability of agent behavior over multiple trials. Our experiments show that even state-of-the-art function calling agents (like gpt-4o) succeed on <50% of the tasks, and are quite inconsistent (pass^8 <25% in retail). Our findings point to the need for methods that can improve the ability of agents to act consistently and follow rules reliably.<|reference_end|>
@article{yao2024$\tau$-bench:,
    title={$\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World
  Domains},
    author={Shunyu Yao, Noah Shinn, Pedram Razavi, Karthik Narasimhan},
    journal={arXiv preprint arXiv:2406.12045},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12045},
    primaryClass={cs.AI cs.CL}
}