@inproceedings{chang-etal-2024-detecting,
    title = "Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics",
    author = "Chang, Tyler A.  and
      Tomanek, Katrin  and
      Hoffmann, Jessica  and
      Thain, Nithum  and
      MacMurray van Liemt, Erin  and
      Meier-Hellstern, Kathleen  and
      Dixon, Lucas",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.423",
    pages = "4729--4743",
    abstract = "We explore a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia{'}s Neutral Point of View (NPOV) principle: acknowledge the absence of a single true answer and surface multiple perspectives. We frame this as retrieval augmented generation, where perspectives are retrieved from a knowledge base and the LLM is tasked with generating a fluent and faithful response from the given perspectives. As a starting point, we use a deterministic retrieval system and then focus on common LLM failure modes that arise during this approach to text generation, namely hallucination and coverage errors. We propose and evaluate three methods to detect such errors based on (1) word-overlap, (2) salience, and (3) LLM-based classifiers. Our results demonstrate that LLM-based classifiers, even when trained only on synthetic errors, achieve high error detection performance, with ROC AUC scores of 95.3{\%} for hallucination and 90.5{\%} for coverage error detection on unambiguous error cases. We show that when no training data is available, our other methods still yield good results on hallucination (84.0{\%}) and coverage error (85.2{\%}) detection.",
}

We explore a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia{'}s Neutral Point of View (NPOV) principle: acknowledge the absence of a single true answer and surface multiple perspectives. We frame this as retrieval augmented generation, where perspectives are retrieved from a knowledge base and the LLM is tasked with generating a fluent and faithful response from the given perspectives. As a starting point, we use a deterministic retrieval system and then focus on common LLM failure modes that arise during this approach to text generation, namely hallucination and coverage errors. We propose and evaluate three methods to detect such errors based on (1) word-overlap, (2) salience, and (3) LLM-based classifiers. Our results demonstrate that LLM-based classifiers, even when trained only on synthetic errors, achieve high error detection performance, with ROC AUC scores of 95.3{\%} for hallucination and 90.5{\%} for coverage error detection on unambiguous error cases. We show that when no training data is available, our other methods still yield good results on hallucination (84.0{\%}) and coverage error (85.2{\%}) detection.
Chang, Tyler A. Tomanek, Katrin Hoffmann, Jessica Thain, Nithum MacMurray van Liemt, Erin Meier-Hellstern, Kathleen Dixon, Lucas
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics