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 introduce a framework to evaluate the quality of benchmarks used for task-oriented conversational agents, which can lead to unreliable evaluations due to inconsistencies, simplistic scenarios, or limited policy coverage. The framework uses Language Model (LLM) judges and provides diagnostics for weaknesses in the benchmarks.

What the Document Says

• The paper discusses a reference-free framework for assessing the quality of benchmarks used for task-oriented conversational agents.
• The framework uses Language Model (LLM) judges to evaluate benchmark consistency, complexity, and policy coverage.
• The proposed metrics help distinguish between benchmark quality levels across various domains and judge models.
• The framework is demonstrated to agree with independent human annotations and to evaluate LLMs of varying capabilities.
• The framework can also handle manually curated benchmarks.

Key Concepts Mentioned

• task-oriented conversational agents
• benchmark quality
• consistency
• complexity
• policy coverage
• Language Model (LLM) judges

Claims Made by the Document

• Benchmark quality is rarely assessed in task-oriented conversational agents.
• Poor benchmarks may lead to unreliable evaluations.
• The proposed framework can assess benchmark consistency, complexity, and policy coverage using LLM judges.

Evidence Used

• The paper states that benchmark quality is rarely assessed.
• The paper discusses potential issues with poor benchmarks leading to unreliable evaluations.
• The paper introduces a framework that uses LLM judges for assessment.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

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.