Beyond Ideal Instruction: A Comprehensive Framework for Evaluating LLMs in Realistic Interactions
arXiv:2606.03318v1 Announce Type: new Abstract: Despite great advances in tool-use capabilities of large language models (LLMs), existing evaluation benchmarks struggle to fully align with real-world scenarios. Such benchmarks mostly rely on simulated idealized user assumptions and lacks experience-oriented evaluation. These limitations fail to account for the ambiguity, uncooperative behaviors, and shifting intentions characteristic of real-world users. To fill this gap, we propose RUT-Bench, a dedicated benchmark designed to assess LLMs under diverse Real-world User Tool calling scenarios. R
The rapid advancement in LLM capabilities necessitates more sophisticated evaluation methods that mirror complex real-world user interactions, moving beyond idealized benchmarks.
Accurate assessment of LLMs in realistic, ambiguous, and uncooperative scenarios is critical for their reliable deployment and integration into diverse applications.
The proposed RUT-Bench shifts the focus of LLM evaluation from ideal conditions to real-world complexities, likely accelerating the development of more robust and adaptable AI agents.
- · LLM developers
- · AI product companies
- · Businesses adopting AI agents
- · AI safety researchers
- · LLMs lacking robustness
- · Developers relying on idealized benchmarks
- · Companies with poorly integrated AI agents
Improved LLM evaluation leads to more resilient and capable AI agents.
Enhanced AI agent performance could accelerate their adoption across various industries, replacing or augmenting human tasks.
The widespread deployment of highly capable AI agents could fundamentally reshape white-collar workflows and the SaaS ecosystem, making human-computer interaction more seamless and autonomous.
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Read at arXiv cs.CL