Summary of Topictag: Automatic Annotation Of Nmf Topic Models Using Chain Of Thought and Prompt Tuning with Llms, by Selma Wanna et al.
TopicTag: Automatic Annotation of NMF Topic Models Using Chain of Thought and Prompt Tuning with LLMs
by Selma Wanna, Ryan Barron, Nick Solovyev, Maksim E. Eren, Manish Bhattarai, Kim Rasmussen, Boian S. Alexandrov
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a methodology for automating topic labeling in documents clustered via non-negative matrix factorization (NMF) with automatic model determination (NMFk). The authors leverage the output of NMFk and employ prompt engineering to utilize large language models (LLMs) to generate accurate topic labels. This approach aims to enhance knowledge management and document organization by providing explicit topic labels, reducing the need for subject matter experts (SMEs) to assign labels manually. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand big collections of text by giving them instructions on how to group similar texts together and label what they’re talking about. Right now, people have to help computers do this by reading through lots of text and saying what topics it covers. This can be time-consuming and not very accurate. The authors developed a way for computers to do this job on their own, using special computer models that can understand what texts are talking about. |
Keywords
» Artificial intelligence » Prompt
