@inproceedings{petrak-etal-2025-towards,
    title = "Towards Automated Error Discovery: A Study in Conversational {AI}",
    author = "Petrak, Dominic  and
      Tran, Thy Thy  and
      Gurevych, Iryna",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.emnlp-main.1/",
    doi = "10.18653/v1/2025.emnlp-main.1",
    pages = "1--23",
    ISBN = "979-8-89176-332-6",
    abstract = "Although LLM-based conversational agents demonstrate strong fluency and coherence, they still produce undesirable behaviors (\textit{errors}) that are challenging to prevent from reaching users during deployment. Recent research leverages large language models (LLMs) to detect errors and guide response-generation models toward improvement. However, current LLMs struggle to identify errors not explicitly specified in their instructions, such as those arising from updates to the response-generation model or shifts in user behavior. In this work, we introduce \textbf{Automated Error Discovery}, a framework for detecting and defining errors in conversational AI, and propose \textbf{SEEED} (Soft Clustering Extended Encoder-Based Error Detection), as an encoder-based approach to its implementation. We enhance the Soft Nearest Neighbor Loss by amplifying distance weighting for negative samples and introduce \textbf{Label-Based Sample Ranking} to select highly contrastive examples for better representation learning. SEEED outperforms adapted baselines{---}including GPT-4o and Phi-4{---}across multiple error-annotated dialogue datasets, improving the accuracy for detecting unknown errors by up to 8 points and demonstrating strong generalization to unknown intent detection."
}