Summary of Scalable Representation Learning For Multimodal Tabular Transactions, by Natraj Raman et al.
Scalable Representation Learning for Multimodal Tabular Transactions
by Natraj Raman, Sumitra Ganesh, Manuela Veloso
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel approach for large language models (LLMs) to effectively process structured tabular data is presented in this paper. The authors highlight that current LLMs struggle to capture inherent relationships and patterns when directly applied to tabular data, particularly in the presence of sparse high-cardinality fields, precise numerical reasoning, and column-heavy tables. To address these challenges, the proposed solution introduces a multi-tier partitioning mechanism, adaptive quantization, and distinct treatments for core-columns and meta-information columns. Additionally, a parameter-efficient decoder is designed to facilitate instruction tuning on LLMs by interleaving transaction and text modalities using adapter layers. The efficacy of this approach is validated on a large-scale dataset of synthetic payments transactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps language models understand structured data better. Right now, these models are great at understanding text, but they struggle when dealing with tables or other types of structured information. The authors propose a new way to process this type of data that includes several key innovations. These include techniques for handling large vocabularies, imposing rules on numerical values, and treating different parts of the table differently. They also design a special decoder that lets the model learn from both text and transaction data at the same time. The authors test their approach using a big dataset of fake payment transactions. |
Keywords
» Artificial intelligence » Decoder » Instruction tuning » Parameter efficient » Quantization
