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Creators/Authors contains: "Feng, Dongji"

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  1. ; ; (, Association for Computational Linguistics)
    Bouamor, Houda; Pino, Juan; Bali, Kalika (Ed.)
    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. 
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  2. ; (, Association for Computational Linguistics)
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