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Found 7 papers, 0 papers with code
Low-resource Taxonomy Enrichment with Pretrained Language Models
Conventional supervised methods for this enrichment task fail to find optimal parents of new terms in low-resource settings where only small taxonomies are available because of overfitting to hierarchical relationships in the taxonomies.
Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering
Finally, based on these observations, we propose a method to mitigate overfitting to specific context quality by introducing bias to the cross-attention distribution, which we demonstrate to be effective in improving the performance of FiD models on different context quality.
Jellyfish: A Large Language Model for Data Preprocessing
This paper explores the utilization of LLMs for data preprocessing (DP), a crucial step in the data mining pipeline that transforms raw data into a clean format conducive to easy processing.
Large Language Models as Data Preprocessors
Large Language Models (LLMs), typified by OpenAI's GPT series and Meta's LLaMA variants, have marked a significant advancement in artificial intelligence.
DeepJoin: Joinable Table Discovery with Pre-trained Language Models
They are either exact solutions whose running time is linear in the sizes of query column and target table repository or approximate solutions lacking precision.
Table Enrichment System for Machine Learning
Data scientists are constantly facing the problem of how to improve prediction accuracy with insufficient tabular data.
Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach
Finding joinable tables in data lakes is key procedure in many applications such as data integration, data augmentation, data analysis, and data market.
