Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Method and 3.1 Phase 1: Taxonomy Generation 3 Method and 3.1 Phase 1: Taxonomy Generation 3.2 Phase 2: LLM-Augmented Text Classification 3.2 Phase 2: LLM-Augmented Text Classification 4 Evaluation Suite and 4.1 Phase 1 Evaluation Strategies 4 Evaluation Suite and 4.1 Phase 1 Evaluation Strategies 4.2 Phase 2 Evaluation Strategies 4.2 Phase 2 Evaluation Strategies 5 Experiments and 5.1 Data 5 Experiments and 5.1 Data 5.2 Taxonomy Generation 5.2 Taxonomy Generation 5.3 LLM-Augmented Text Classification 5.3 LLM-Augmented Text Classification 5.4 Summary of Findings and Suggestions 5.4 Summary of Findings and Suggestions 6 Discussion and Future Work, and References 6 Discussion and Future Work, and References A. Taxonomies A. Taxonomies B. Additional Results B. Additional Results C. Implementation Details C. Implementation Details D. Prompt Templates D. Prompt Templates 6 DISCUSSION AND FUTURE WORK This work has the potential to create significant impact for research and application of AI technologies in text mining. Our framework has demonstrated the ability to use LLMs as taxonomy generators, as well as data labelers and evaluators. These automations could lead to significant efficiency gains and cost savings for a variety of domains and applications that rely on understanding, structuring and analyzing massive volumes of unstructured text. It could also broadly democratize the process of mining knowledge from text, empowering non-expert users and enterprises to interact with and interpret their data through natural language, thereby leading to better insights and data-driven decision making for a range of industries and sectors. Additionally, our framework and research findings relate to other work that leverages LLMs for taxonomy creation and text clustering, and has important empirical lessons for the efficient use of instruction-following models in these scenarios. Despite these initial successes, there are some important challenges and future directions that are worth exploring. As we have already noted, LLMs are expensive and slow. In future work, we hope to explore ways to improve the speed, efficiency and robustness of our framework, through hybrid approaches that further explore the combination of LLMs with embedding-based methods, or model distillation that fine-tunes a smaller model through instructions from a larger one. Evaluation continues to be a crucial and open challenge for future work, and we plan to explore ways of performing more robust LLM-aided evaluations in future work, for example by fine-tuning a model to expand its reasoning capabilities beyond pairwise judgement tasks. While this work has focused largely on text mining in the conversational domain, we also hope to explore the extensibility of our framework to other domains as well. Finally, many domains have ethical considerations from the perspective of privacy and security that must be taken into account when performing large-scale automated text mining, and we hope to engage with these challenges more deeply in future work. REFERENCES [1] Charu C Aggarwal and ChengXiang Zhai. 2012. A survey of text clustering algorithms. Mining text data (2012), 77–128. [2] David Arthur, Sergei Vassilvitskii, et al. 2007. k-means++: The advantages of careful seeding. In Soda, Vol. 7. 1027–1035. [3] Léon Bottou. 1998. Online algorithms and stochastic approximations. Online learning in neural networks (1998). [4] B Barla Cambazoglu, Leila Tavakoli, Falk Scholer, Mark Sanderson, and Bruce Croft. 2021. An intent taxonomy for questions asked in web search. 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Association for Computational Linguistics, Singapore, 10626–10649. https://doi.org/10.18653/v1/2023.emnlp-main.657 https://doi.org/10.18653/v1/2023.emnlp-main.657 [31] Anuradha Welivita and Pearl Pu. 2020. A Taxonomy of Empathetic Response Intents in Human Social Conversations. In Proceedings of the 28th International Conference on Computational Linguistics. 4886–4899. [32] Qingkai Zeng, Jinfeng Lin, Wenhao Yu, Jane Cleland-Huang, and Meng Jiang. 2021. Enhancing taxonomy completion with concept generation via fusing relational representations. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2104–2113. [33] Chao Zhang, Fangbo Tao, Xiusi Chen, Jiaming Shen, Meng Jiang, Brian Sadler, Michelle Vanni, and Jiawei Han. 2018. Taxogen: Unsupervised topic taxonomy construction by adaptive term embedding and clustering. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2701–2709. [34] Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang. 2023. Can Large Language Models Transform Computational Social Science? arXiv preprint arXiv:2305.03514 (2023). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors:
(1) Mengting Wan, Microsoft Corporation and Microsoft Corporation;
(2) Tara Safavi (Corresponding authors), Microsoft Corporation;
(3) Sujay Kumar Jauhar, Microsoft Corporation;
(4) Yujin Kim, Microsoft Corporation;
(5) Scott Counts, Microsoft Corporation;
(6) Jennifer Neville, Microsoft Corporation;
(7) Siddharth Suri, Microsoft Corporation;
(8) Chirag Shah, University of Washington and Work done while working at Microsoft;
(9) Ryen W. White, Microsoft Corporation;
(10) Longqi Yang, Microsoft Corporation;
(11) Reid Andersen, Microsoft Corporation;
(12) Georg Buscher, Microsoft Corporation;
(13) Dhruv Joshi, Microsoft Corporation;
(14) Nagu Rangan, Microsoft Corporation. Authors: Authors: (1) Mengting Wan, Microsoft Corporation and Microsoft Corporation; (2) Tara Safavi (Corresponding authors), Microsoft Corporation; (3) Sujay Kumar Jauhar, Microsoft Corporation; (4) Yujin Kim, Microsoft Corporation; (5) Scott Counts, Microsoft Corporation; (6) Jennifer Neville, Microsoft Corporation; (7) Siddharth Suri, Microsoft Corporation; (8) Chirag Shah, University of Washington and Work done while working at Microsoft; (9) Ryen W. White, Microsoft Corporation; (10) Longqi Yang, Microsoft Corporation; (11) Reid Andersen, Microsoft Corporation; (12) Georg Buscher, Microsoft Corporation; (13) Dhruv Joshi, Microsoft Corporation; (14) Nagu Rangan, Microsoft Corporation.

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