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Source note2 min read

Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents

Source

arXiv

arxiv.org/abs/2608.06329v1

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Source

Plain-Language Brief

The authors present a new framework to evaluate the quality of benchmarks used to test conversational agents. The framework uses language models (LLMs) as judges to assess the consistency, complexity, and policy coverage of benchmarks. They demonstrate that their metrics can distinguish between different levels of benchmark quality across various domains and judge models.

What the Document Says

  • The text discusses a framework for evaluating the quality of benchmarks used in task-oriented conversational agents.
  • The framework uses LLM judges to assess benchmark consistency, complexity, and policy coverage.
  • They validate the framework by comparing it with independent human annotations and evaluations of LLMs of varying capabilities.
  • The authors also test the framework on benchmarks subjected to quality-degrading perturbations.
  • Across domains and judge models, the proposed metrics consistently distinguish between benchmark quality levels.
  • The framework is shown to be applicable to manually curated benchmarks.

Key Concepts Mentioned

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

Claims Made by the Document

  • Poor benchmarks may lead to unreliable evaluations.
  • The proposed framework can assess the quality of synthetic and manually curated conversational-agent benchmarks.

Evidence Used

  • The text discusses how poor benchmarks can lead to unreliable evaluations.
  • The authors demonstrate that their metrics can distinguish between benchmark quality levels across various domains and judge models.

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.