Official Repository of "LLM × DATA" Survey Paper

[ICML 2024] LESS: Selecting Influential Data for Targeted Instruction Tuning

DSIR large-scale data selection framework for language model training

A Survey on Data Selection for Language Models

⛔ [DEPRECATED] Adapt Transformer-based language models to new text domains

🔥[VLDB'26] Official repository for the paper "LEAD: Iterative Data Selection for Efficient LLM Instruction Tuning".

Code for ACL 2025 Main paper "Data Whisperer: Efficient Data Selection for Task-Specific LLM Fine-Tuning via Few-Shot In-Context Learning".

InstructionGPT-4

[ACL 2025 main] SCAR: Data Selection via Style Consistency-Aware Response Ranking for Efficient Instruction-Tuning of Large Language Models

DataFlex is a data-centric training framework that enhances model performance by either selecting the most influential samples, optimizing their weights, or adjusting their mixing ratios.

[ACL2025 Findings] Official code for MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space

[ACL 2023] The code for our ACL'23 paper Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach

Implementation of TSDS: Data Selection for Task-Specific Model Finetuning. An optimal-transport framework for selecting domain-specific and task-specific training data to improve LLM finetuning and instruction tuning.

This is an official repository for "Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources" (NeurIPS 2023).

Enhancing Efficiency in Multidevice Federated Learning through Data Selection

Enhanced spatio-temporal electric load forecasts with less data using active deep learning

Keras sentence classification

Dynamic Transfer Learning for Low-Resource Neural Machine Translation

Repository for the experiments in my paper accepted to the CLIN Journal: "Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts"

Code for NeurIPS 2023 Paper (Imitation Learning from Imperfection: Theoretical Justifications and Algorithms)