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        <title>Zero-Shot Multi-Label Topic Inference with Sentence Encoders and LLMs</title>
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        <namePart type="given">Souvika</namePart>
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        <namePart type="family">Karmaker Santu</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Multi-Label Topic Inference with Sentence Encoders and LLMs
%A Sarkar, Souvika
%A Feng, Dongji
%A Karmaker Santu, Shubhra Kanti
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F sarkar-etal-2023-zero
%X 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.
%R 10.18653/v1/2023.emnlp-main.1008
%U https://aclanthology.org/2023.emnlp-main.1008/
%U https://doi.org/10.18653/v1/2023.emnlp-main.1008
%P 16218-16233

Markdown (Informal)

[Zero-Shot Multi-Label Topic Inference with Sentence Encoders and LLMs](https://aclanthology.org/2023.emnlp-main.1008/) (Sarkar et al., EMNLP 2023)

• Zero-Shot Multi-Label Topic Inference with Sentence Encoders and LLMs (Sarkar et al., EMNLP 2023)

ACL

• Souvika Sarkar, Dongji Feng, and Shubhra Kanti Karmaker Santu. 2023. Zero-Shot Multi-Label Topic Inference with Sentence Encoders and LLMs. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 16218–16233, Singapore. Association for Computational Linguistics.