AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
TabClean is presented, a cost-efficient and training-free tabular data cleaning system in which LLM agents synthesize reusable code for error detection and correction, making LLM usage a largely one-time rather than recurring cost.
Synthetic tabular data is increasingly used to support data sharing and machine learning development, where access to real data is restricted or sensitive. However, existing tabular data generators often require dataset-specific training or fine-tuning, data preprocessing, and extensive hyperparameter tuning to achieve...
These findings endorse the viability of LLMs as a feasible alternative for data error detection; nonetheless, challenges such as hallucination and cost aspects remain, especially regarding the resources necessary for the effective implementation of these models in practical applications.
Localized TabICLv2 introduces a method that reduces the inference cost of TabICLv2 by retrieving only the k nearest training neighbours for each test point, measured by similarity in the model's Stage 2 row-representation space, rather than using the full training context.
Predicting missing cell values in tabular data is a fundamental problem in data cleaning. While state-of-the-art reasoning models show great promise in predicting missing values in tables, by reasoning holistically across rows and columns, they are costly to deploy at scale and tend to be overconfident, often generatin...
Decompose--Enhance--Correct (Decompose--Enhance--Correct), a visual-consistency-guided agentic framework that improves frozen table parsers without retraining is proposed, which derives a 1,977-table Consensus-Hard Set from 4,556 candidates through offline metrics and cross-model consensus.
PreRet, a framework that jointly optimizes preprocessing pipeline selection and neural retrieval through differentiable optimization through differentiable optimization, is proposed, enabling end-to-end gradient-based learning over a search space of 700 configurations spanning text cleaning, pre-tokenization, chunking,...
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