ARXIV:2603.10570 · CHATBOT EVALUATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

End-to-End Chatbot Evaluation with Adaptive Reasoning and Uncertainty Filtering

arXiv

An end-to-end automatic evaluator for domain-specific chatbots that reduces human review effort and enhances scalability.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain An end-to-end automatic evaluator for domain-specific chatbots that reduces human review effort and enhances scalability.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

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PROBLEM

An end-to-end automatic evaluator for domain-specific chatbots that reduces human review effort and enhances scalability. Reliable evaluation is therefore critical, yet manual review is costly and existing frameworks often depend on curated test sets…

METHOD

Full abstract

Large language models (LLMs) combined with retrieval augmented generation have enabled the deployment of domain-specific chatbots, but these systems remain prone to generating unsupported or incorrect answers. Reliable evaluation is therefore critical, yet manual review is costly and existing frameworks often depend on curated test sets and static metrics, limiting scalability. We propose an end-to-end automatic evaluator designed to substantially reduce human effort. Our system generates Q\&A pairs directly from the underlying knowledge base, uses LLMs to judge chatbot responses against reference answers, and applies confidence-based filtering to highlight uncertain cases. Applied to a Vietnamese news dataset, the evaluator achieves high agreement with human judgments while significantly lowering review overhead. The framework is modular and language-agnostic, making it readily adaptable to diverse domains. This work introduces a practical, scalable solution for evaluating chatbots with minimal reliance on manual intervention.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Applied to a Vietnamese news dataset, the evaluator achieves high agreement with human judgments while significantly lowering review overhead.

WHY NOW

Chatbot Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10.

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Opportunity summary

Score7.0

PainAn end-to-end automatic evaluator for domain-specific chatbots that reduces human review effort and enhances scalability.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

An end-to-end automatic evaluator for domain-specific chatbots that reduces human review effort and enhances scalability.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competitive landscape

An end-to-end automatic evaluator for domain-specific chatbots that reduces human review effort and enhances scalability.

Segment

Chatbot Evaluation

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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Competitive landscape

An end-to-end automatic evaluator for domain-specific chatbots that reduces human review effort and enhances scalability.

Segment

Chatbot Evaluation

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

End-to-End Chatbot Evaluation with Adaptive Reasoning and Uncertainty Filtering

End-to-End Chatbot Evaluation with Adaptive Reasoning and Uncertainty Filtering

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