[2406.12045] $τ$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
Computer Science > Artificial Intelligence
arXiv:2406.12045 (cs)
[Submitted on 17 Jun 2024]
Title:$τ$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
Authors:Shunyu Yao, Noah Shinn, Pedram Razavi, Karthik Narasimhan
View a PDF of the paper titled $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains, by Shunyu Yao and 3 other authors
Abstract: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.
Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: | arXiv:2406.12045 [cs.AI] (or arXiv:2406.12045v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2406.12045 Focus to learn more arXiv-issued DOI via DataCite
Submission history
From: Karthik Narasimhan [view email] [v1] Mon, 17 Jun 2024 19:33:08 UTC (647 KB)
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