Don't Forget Your ABC's: Evaluating the State-of-the-Art in Chat-Oriented Dialogue Systems
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Abstract (arXiv)
Despite tremendous advancements in dialogue systems, stable evaluation still requires human judgments producing notoriously high-variance metrics due to their inherent subjectivity. Moreover, methods and labels in dialogue evaluation are not fully standardized, especially for open-domain chats, with a lack of work to compare and assess the validity of those approaches. The use of inconsistent evaluation can misinform the performance of a dialogue system, which becomes a major hurdle to enhance it. Thus, a dimensional evaluation of chat-oriented open-domain dialogue systems that reliably measures several aspects of dialogue capabilities is desired. This paper presents a novel human evaluation method to estimate the rates of many dialogue system behaviors. Our method is used to evaluate four state-of-the-art open-domain dialogue systems and compared with existing approaches. The analysis demonstrates that our behavior method is more suitable than alternative Likert-style or comparative approaches for dimensional evaluation of these systems.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 44 of its 73 references that are in the map. First posted on arXiv 18 Dec 2022.
Builds on
44 of 73 references.
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32 more
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Built on by
Citing papers per year: 2024: 5; 2025: 3; 2026: 1.
9 papers.
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- 2024Dialogue You Can Trust: Human and AI Perspectives on Generated Conversationscited by 0
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- 2026Measuring Semantic Progress in Multi-turn Dialogue via Information Gain10 Jun 2026
- 2025LLMs Get Lost In Multi-Turn Conversation9 May 2025
- 2025Detect, Explain, Escalate: Sustainable Dialogue Breakdown Management for LLM Agents26 Apr 2025
- 2025Correlating and Predicting Human Evaluations of Language Models from Natural Language Processing Benchmarks24 Feb 2025
- 2024SCBench: A KV Cache-Centric Analysis of Long-Context Methods13 Dec 2024
- 2024Socio-Emotional Response Generation: A Human Evaluation Protocol for LLM-Based Conversational Systems26 Nov 2024
- 2024Dialogue You Can Trust: Human and AI Perspectives on Generated Conversations3 Sep 2024
- 2024The Challenges of Evaluating LLM Applications: An Analysis of Automated, Human, and LLM-Based Approaches5 Jun 2024
- 2024ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AI27 Jan 2024