fPLSA: Learning Semantic Structures in Document Collections Using Foundation Models

• URL: http://arxiv.org/abs/2410.05481v1
• Date: Mon, 7 Oct 2024 20:25:52 GMT
• Title: fPLSA: Learning Semantic Structures in Document Collections Using Foundation Models
• Authors: Weijia Xu, Nebojsa Jojic, Nicolas Le Roux,
• Abstract summary: We introduce fPLSA, a foundation-model-based Probabilistic Latent Semantic Analysis (PLSA) method. PLSA iteratively clusters and tags document segments based on document-level contexts. Our experiments on story writing, math, and multi-step reasoning datasets demonstrate that fPLSA tags help reconstruct the original texts better than existing tagging methods.
• Score: 19.099810900404357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans have the ability to learn new tasks by inferring high-level concepts from existing solution, then manipulating these concepts in lieu of the raw data. Can we automate this process by deriving latent semantic structures in a document collection using foundation models? We introduce fPLSA, a foundation-model-based Probabilistic Latent Semantic Analysis (PLSA) method that iteratively clusters and tags document segments based on document-level contexts. These tags can be used to model the structure of given documents and for hierarchical sampling of new texts. Our experiments on story writing, math, and multi-step reasoning datasets demonstrate that fPLSA tags help reconstruct the original texts better than existing tagging methods. Moreover, when used for hierarchical sampling, fPLSA produces more diverse outputs with a higher likelihood of hitting the correct answer than direct sampling and hierarchical sampling with existing tagging methods.

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