@inproceedings{afzal-etal-2024-towards,
    title = "Towards Optimizing and Evaluating a Retrieval Augmented {QA} Chatbot using {LLM}s with Human-in-the-Loop",
    author = "Afzal, Anum  and
      Kowsik, Alexander  and
      Fani, Rajna  and
      Matthes, Florian",
    editor = "Dragut, Eduard  and
      Li, Yunyao  and
      Popa, Lucian  and
      Vucetic, Slobodan  and
      Srivastava, Shashank",
    booktitle = "Proceedings of the Fifth Workshop on Data Science with Human-in-the-Loop (DaSH 2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.dash-1.2",
    pages = "4--16",
    abstract = "Large Language Models have found application in various mundane and repetitive tasks including Human Resource (HR) support. We worked with the domain experts of a large multinational company to develop an HR support chatbot as an efficient and effective tool for addressing employee inquiries. We inserted a human-in-the-loop in various parts of the development cycles such as dataset collection, prompt optimization, and evaluation of generated output. By enhancing the LLM-driven chatbot{'}s response quality and exploring alternative retrieval methods, we have created an efficient, scalable, and flexible tool for HR professionals to address employee inquiries effectively. Our experiments and evaluation conclude that GPT-4 outperforms other models and can overcome inconsistencies in data through internal reasoning capabilities. Additionally, through expert analysis, we infer that reference-free evaluation metrics such as G-Eval and Prometheus demonstrate reliability closely aligned with that of human evaluation.",
}