Small Model-Based Data Selection

Heuristics-Based Data Filtering for Fine-Tuning

Prioritizing Influential Data for Fine-Tuning

A development team fine-tunes a large language model on a massive, newly-generated dataset of 1 million instruction-response pairs. After training, they find the model's performance is poor, often generating repetitive, nonsensical, or factually incorrect answers. Which of the following is the most likely root cause of this issue and the best initial strategy to address it?

Evaluating a Data Filtering Strategy

A team is preparing a large, synthetically-generated dataset for fine-tuning a language model. They suspect the dataset has several quality issues. Match each potential data quality problem with the primary goal of a filtering method designed to address it.

Your company is rolling out an instruction-tuned L...

You lead an LLM enablement team building an instru...

You’re leading an LLM platform team building an in...

Your company is building an internal IT helpdesk a...

Deciding Whether (and How) to Use Weak-Model Synthetic Data for Instruction Fine-Tuning

Diagnosing and Fixing a Synthetic Instruction-Tuning Data Flywheel That Degrades Model Behavior

Designing a Synthetic Instruction Fine-Tuning Pipeline Under Budget and Quality Constraints

Stabilizing an Instruction-Tuned Support Assistant When Synthetic Data Conflicts with Human Policy

Selecting and Filtering Self-Generated Instruction Data When Bootstrapping a Strong Model from a Weak Supervisor

Choosing a Weak-Model + Self-Instruct Data Strategy for Instruction Fine-Tuning Without Regressions

Efficiency Benefits of Data Selection in Fine-Tuning

Alpagasus Data Selection System