Survey Maps Evaluation Methods for LLM-Based Agents
A recent study published on arXiv presents the inaugural extensive examination of evaluation techniques for agents powered by large language models (LLMs). These agents signify a transformative advancement in artificial intelligence, allowing for autonomous planning, reasoning, and tool utilization in ever-changing settings. The research evaluates five key areas: fundamental LLM skills (planning and tool usage), benchmarks tailored for specific applications (such as web and software engineering agents), assessments of generalist agents, essential benchmark dimensions, and evaluation frameworks. It notes a trend towards more realistic and demanding evaluations with regularly updated benchmarks, while also pointing out significant deficiencies in measuring cost-effectiveness, safety, and robustness, alongside the necessity for detailed, scalable evaluation approaches.
Key facts
- arXiv:2503.16416v2 is a comprehensive survey on evaluation of LLM-based agents
- The survey covers five perspectives: core LLM capabilities, application-specific benchmarks, generalist agents, benchmark dimensions, and evaluation frameworks
- Current trends include a shift toward more realistic and challenging evaluations
- Critical gaps identified include cost-efficiency, safety, and robustness assessment
- The paper emphasizes the need for fine-grained, scalable evaluation methods
Entities
Institutions
- arXiv