Summary of Fplsa: Learning Semantic Structures in Document Collections Using Foundation Models, by Weijia Xu et al.
fPLSA: Learning Semantic Structures in Document Collections Using Foundation Models
by Weijia Xu, Nebojsa Jojic, Nicolas Le Roux
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes an innovative method called fPLSA that utilizes foundation models to automate the process of learning new tasks by inferring high-level concepts from existing solutions. The authors develop a Probabilistic Latent Semantic Analysis (PLSA) approach that iteratively clusters and tags document segments based on document-level contexts, enabling the modeling of given documents’ structure and hierarchical sampling of new texts. The experiments conducted on story writing, math, and multi-step reasoning datasets demonstrate that fPLSA tags can reconstruct original texts more effectively than existing tagging methods. Moreover, when used for hierarchical sampling, fPLSA produces more diverse outputs with a higher likelihood of hitting the correct answer compared to direct sampling and hierarchical sampling with existing tagging methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to learn new tasks by understanding how people do things. Right now, humans can learn new skills by looking at what experts already know and doing it themselves. Can we teach computers to do this too? The authors came up with a way to use “foundation models” (like language models) to understand the structure of documents and generate new texts that are similar in style and content. They tested their method on different datasets and found that it can reconstruct original texts better than other methods, and also generate more diverse and correct outputs when asked to write something new. |
Keywords
* Artificial intelligence * Likelihood
