Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Noam Koren, Roy Bar-Haim, Abigail Goldsteen
• Venue: arXiv: cs.CL
• Published: 2026-08-06
• Collected: 2026-08-07
• URL: http://arxiv.org/abs/2608.06329v1
• PDF: https://arxiv.org/pdf/2608.06329v1
• DOI: UNKNOWN

Plain-Language Brief

Researchers have introduced a new framework to evaluate the quality of benchmarks used for task-oriented conversational agents. The framework uses language models (LLMs) to assess the consistency, complexity, and policy coverage of benchmarks, providing diagnostics of weaknesses. The framework was validated through agreement with human annotations and by testing it on LLMs of varying capabilities and quality-degraded benchmarks.

What the Document Says

• A new reference-free framework is introduced for evaluating the quality of benchmarks used in task-oriented conversational agents.
• The framework uses Language Learning Models (LLMs) to assess the consistency, complexity, and policy coverage of benchmarks.
• The framework provides diagnostics of weaknesses.
• The framework was validated by demonstrating agreement with independent human annotations and by evaluating LLMs of varying capabilities and quality-degraded benchmarks.

Key Concepts Mentioned

• task-oriented conversational agents
• benchmark evaluation
• consistency
• complexity
• policy coverage
• LLM judges

Claims Made by the Document

• Benchmark quality is rarely assessed.
• Poor benchmarks may lead to unreliable evaluations.
• The proposed metrics consistently distinguish between benchmark quality levels.

Evidence Used

• We introduce a reference-free framework...

Methods / Approach

• Using LLM judges to assess benchmark consistency, complexity, and policy coverage.

Limitations Stated by the Authors

• UNKNOWN (Not stated in source)

Limitations Noted by the Agent

Agent interpretation — not from the source.

• The model does not have the ability to validate claims beyond what is stated in the provided text.

Open Questions

• What are the specific metrics used by the framework?
• How was agreement with human annotations measured?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The text does not provide specific details about the metrics or methods used for agreement measurement.

Raw Abstract / Excerpt

Task-oriented conversational agents are evaluated using curated or automatically generated benchmarks, yet benchmark quality is rarely assessed. Poor benchmarks may contain inconsistent tasks, simplistic scenarios, or limited policy coverage, leading to unreliable evaluations. We introduce a reference-free framework that uses LLM judges to assess benchmark consistency, complexity, and policy coverage, while providing actionable diagnostics of weaknesses. We validate the framework by demonstrating agreement with independent human annotations and by evaluating benchmarks generated by LLMs of varying capabilities, as well as benchmarks subjected to controlled quality-degrading perturbations. Across domains and judge models, the proposed metrics consistently distinguish between benchmark quality levels. We further demonstrate the framework's applicability to manually curated benchmarks. Our framework offers a practical approach for evaluating synthetic and manually curated conversational-agent benchmarks.