Summary of Claim Your Data: Enhancing Imputation Accuracy with Contextual Large Language Models, by Ahatsham Hayat and Mohammad Rashedul Hasan
CLAIM Your Data: Enhancing Imputation Accuracy with Contextual Large Language Models
by Ahatsham Hayat, Mohammad Rashedul Hasan
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 proposes Contextual Language model for Accurate Imputation Method (CLAIM), which leverages pre-trained large language models (LLMs) to tackle missing data challenges in tabular datasets. Unlike traditional imputation methods, CLAIM uses natural language descriptors to fill missing values by transforming datasets into contextualized formats that align with LLMs’ capabilities. The approach involves generating missing value descriptors using LLMs and then fine-tuning the model on the enriched dataset for improved performance. Evaluations across diverse datasets and missingness patterns show CLAIM’s superiority over existing imputation techniques. Additionally, the study highlights the importance of contextual accuracy in enhancing LLM performance for data imputation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CLAIM is a new way to fill in missing data by using large language models (LLMs) to create words that describe what’s missing. This helps the model understand the context and make better guesses about what should go in those empty spaces. The researchers tested CLAIM on different datasets and found it works much better than other methods for filling in missing data. This could be really useful for people working with data, as it can help them get more accurate results and avoid mistakes. |
Keywords
» Artificial intelligence » Fine tuning » Language model
