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.- Anthology ID:
- 2023.emnlp-main.1008
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 16218–16233
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.1008
- DOI:
- 10.18653/v1/2023.emnlp-main.1008
- Cite (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.
- Cite (Informal):
- Zero-Shot Multi-Label Topic Inference with Sentence Encoders and LLMs (Sarkar et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.emnlp-main.1008.pdf