@inproceedings{sarkar-etal-2023-zero,
    title = "Zero-Shot Multi-Label Topic Inference with Sentence Encoders and {LLM}s",
    author = "Sarkar, Souvika  and
      Feng, Dongji  and
      Karmaker Santu, Shubhra Kanti",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2023.emnlp-main.1008/",
    doi = "10.18653/v1/2023.emnlp-main.1008",
    pages = "16218--16233",
    abstract = "In this paper, we conducted a comprehensive study with the latest Sentence Encoders and Large Language Models (LLMs) on the challenging task of ``definition-wild zero-shot topic inference'', where users define or provide the topics of interest in real-time. Through extensive experimentation on seven diverse data sets, we observed that LLMs, such as ChatGPT-3.5 and PaLM, demonstrated superior generality compared to other LLMs, e.g., BLOOM and GPT-NeoX. Furthermore, Sentence-BERT, a BERT-based classical sentence encoder, outperformed PaLM and achieved performance comparable to ChatGPT-3.5."
}