Don't Forget Your ABC's: Evaluating the State-of-the-Art in Chat-Oriented Dialogue Systems
Sarah E. Finch, James D. Finch, Jinho D. Choi
Abstract
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 manypasted macro 'LN' 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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers5
- LLMs Get Lost In Multi-Turn ConversationPhilippe Laban, Hiroaki Hayashi, Yingbo Zhou, Jennifer NevilleICLR 2026 · 491 citations
- Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational SearchHideaki Joko, Shubham Chatterjee, Andrew Ramsay, Arjen P. de Vries et al.SIGIR 2024 · 24 citations
- Dual-Axis Generative Reward Model Toward Semantic and Turn-taking Robustness in Interactive Spoken Dialogue ModelsYifu Chen, Shengpeng Ji, Zhengqing Liu, Qian Chen et al.ACL 2026 · 7 citations
- Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention RewardsMing Li, Pei Chen, Zhenhao Zhang, Tao Yang et al.ACL 2026 · 3 citations
- SCBench: A KV Cache-Centric Analysis of Long-Context MethodsYucheng Li, Huiqiang Jiang, Qianhui Wu, Xufang Luo et al.ICLR 2025
Builds on24
- Beyond Goldfish Memory: Long-Term Open-Domain ConversationJing Xu, Arthur Szlam, Jason WestonACL 2022 · 329 citations
- CEM: Commonsense-Aware Empathetic Response GenerationSahand Sabour, Chujie Zheng, Minlie HuangAAAI 2022 · 196 citations
- Dense Passage Retrieval for Open-Domain Question AnsweringVladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis et al.EMNLP 2020 · 142 citations
- Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood TrainingMargaret Li, Stephen Roller, Ilia Kulikov, Sean Welleck et al.ACL 2020 · 120 citations
- : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question AnsweringOr Honovich, Leshem Choshen, Roee Aharoni, Ella Neeman et al.EMNLP 2021 · 101 citations
Related papers
- Achieving Reliable Human Assessment of Open-Domain Dialogue SystemsTianbo Ji, Yvette Graham, Gareth J. F. Jones, Chenyang Lyu et al.ACL 2022
- MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue EvaluationChen Zhang, Luis Fernando D'Haro, Thomas Friedrichs, Haizhou LiAAAI 2022 · 22 citations
- Proxy Indicators for the Quality of Open-domain DialoguesRostislav Nedelchev, Jens Lehmann, Ricardo UsbeckEMNLP 2021
- FineD-Eval: Fine-grained Automatic Dialogue-Level EvaluationChen Zhang, Luis Fernando D'Haro, Qiquan Zhang, Thomas Friedrichs et al.EMNLP 2022 · 13 citations
- Just Adjust One Prompt: Enhancing In-Context Dialogue Scoring via Constructing the Optimal Subgraph of Demonstrations and PromptsJiashu Pu, Ling Cheng, Lu Fan, Tangjie Lv et al.EMNLP 2023 · 2 citations
