| Challenge: | Using language models to perform complex interactive tasks is becoming more common with the rapid progress in natural language processing (NLP) models. |
| Approach: | They develop an evaluation toolkit that enables human-bot interactions as part of the evaluation process. |
| Outcome: | The evaluation toolkit enables human-bot interactions as part of the evaluation process, rather than making judgements for a static input. |
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| Challenge: | open-domain dialog systems are difficult to evaluate due to lack of standardization and standardization in evaluation procedures. |
| Approach: | They propose a framework for human evaluation of chatbots that augments existing tools . researchers can submit their trained models to the ChatEval web interface . reproducibility and model assessment for opendomain dialog systems is challenging . |
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LEGOEval: An Open-Source Toolkit for Dialogue System Evaluation via Crowdsourcing (2021.acl-demo)
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| Challenge: | Currently, researchers use automatic metrics and human evaluation to evaluate dialogue systems. |
| Approach: | They propose to use a Python API to easily evaluate dialogue systems using Amazon Mechanical Turk. |
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FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models (2024.emnlp-demo)
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Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Zhengran Zeng, Wei Ye, Jindong Wang, Yue Zhang, Shikun Zhang
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Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems (2020.emnlp-main)
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Jan Deriu, Don Tuggener, Pius von Däniken, Jon Ander Campos, Alvaro Rodrigo, Thiziri Belkacem, Aitor Soroa, Eneko Agirre, Mark Cieliebak
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CourtEval: A Courtroom-Based Multi-Agent Evaluation Framework (2025.findings-acl)
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| Challenge: | Existing automated evaluation metrics like ROUGE and BLEU show low correlation with human judgments. |
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AutoEval-ToD: Automated Evaluation of Task-oriented Dialog Systems (2025.naacl-long)
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| Challenge: | Current evaluation methodologies heavily depend on human annotators, which can be inefficient, subjective, and expensive to scale. |
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ChatMatch: Evaluating Chatbots by Autonomous Chat Tournaments (2022.acl-long)
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| Challenge: | Existing automated evaluation systems of chatbots rely on static chat scripts as ground truth, which is hard to obtain. |
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ChatHF: Collecting Rich Human Feedback from Real-time Conversations (2024.emnlp-demo)
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| Challenge: | We present an interactive framework for chatbot evaluation that integrates configurable annotation within a chat interface. |
| Approach: | They propose an interactive framework for chatbot evaluation that integrates configurable annotation within a chat interface. |
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S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Model (2024.naacl-long)
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| Challenge: | Existing benchmarks fail to evaluate extremely long-context LLMs or analyze their limitations. |
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ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents (2026.acl-long)
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| Challenge: | Existing studies on proactive dialogue models focus on domain-specific or task-oriented scenarios, which leads to fragmented evaluations and limits the comprehensive exploration of models’ proactive dialogue abilities. |
| Approach: | They propose a framework for evaluating proactive dialogue capabilities of large language models that decomposes proactive dialogue into target planning and dialogue guidance, establishing evaluation metrics across various domains. |
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