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

The paper introduces a reference-free framework to evaluate the quality of task-oriented conversational agent benchmarks using LLM (Large Language Model) judges. The framework assesses consistency, complexity, and policy coverage of the benchmark, providing diagnostics for weaknesses. It has been validated with agreement from independent human annotations and various LLMs capabilities.

What the Document Says

• The paper discusses a framework to evaluate task-oriented conversational agent benchmarks using LLM judges.
• The framework assesses the consistency, complexity, and policy coverage of the benchmark.
• It provides diagnostics for weaknesses in the benchmark.
• Validation has been done with agreement from independent human annotations and various LLMs capabilities.

Key Concepts Mentioned

• Task-oriented conversational agents
• Benchmarks
• Evaluation
• Large Language Model (LLM)
• Consistency
• Complexity
• Policy coverage

Claims Made by the Document

• Existing benchmarks for task-oriented conversational agents may be poor quality.
• The proposed framework can assess the quality of these benchmarks.
• The framework uses LLM judges to evaluate consistency, complexity, and policy coverage.
• Validation has been done with agreement from independent human annotations and various LLMs capabilities.

Evidence Used

• The paper explains the proposed framework and its components.

Methods / Approach

• Using a reference-free framework to evaluate benchmark quality.
• Involving LLM judges in the evaluation process.

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The paper doesn't explicitly mention any limitations of the proposed method, but as a helpful assistant I can suggest that one potential limitation could be the reliability and generalizability of the LLM judges.

Open Questions

• What are the specific details of the validation process with independent human annotations?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper does not provide detailed information about the specific validation process with independent human annotations.

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.