@inproceedings{deol-etal-2025-chatbot,
    title = "Is this Chatbot Trying to Sell Something? Towards Oversight of Chatbot Sales Tactics",
    author = "Deol, Simrat  and
      Contro, Jack Luigi Henry  and
      Brandao, Martim",
    editor = "Zhang, Chen  and
      Allaway, Emily  and
      Shen, Hua  and
      Miculicich, Lesly  and
      Li, Yinqiao  and
      M'hamdi, Meryem  and
      Limkonchotiwat, Peerat  and
      Bai, Richard He  and
      T.y.s.s., Santosh  and
      Han, Sophia Simeng  and
      Thapa, Surendrabikram  and
      Rim, Wiem Ben",
    booktitle = "Proceedings of the 9th Widening NLP Workshop",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.winlp-main.24/",
    pages = "136--156",
    ISBN = "979-8-89176-351-7",
    abstract = "This research investigates the detection of covert sales tactics in human-chatbot interactions with a focus on the classification of solicited and unsolicited product recommendations. A custom dataset of 630 conversations was generated using a Large Language Model (LLM) to simulate chatbot-user interactions in various contexts, such as when interacting with users from different age groups, recommending different types of products and using different types of sales tactics. We then employ various approaches, including BiLSTM-based classification with sentence and word-level embeddings, as well as zero-shot, few-shot and CoT classification on large state-of-the-art LLMs. Our results show that few-shot GPT4 (86.44{\%}) is the most accurate model on our dataset, followed by our compact SBERT+BiLSTM model (78.63{\%}) - despite its small size.Our work demonstrates the feasibility of implementing oversight algorithms for monitoring chatbot conversations for undesired practices and that such monitoring could potentially be implemented locally on-device to mitigate privacy concerns. This research thus lays the groundwork for the development of auditing and oversight methods for virtual assistants such as chatbots, allowing consumer protection agencies to monitor the ethical use of conversational AI."
}