A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't)
arXiv:2602.14696v2 Announce Type: replace Abstract: Instruction fine-tuning of large language models (LLMs) often involves selecting a subset of instruction training data from a large candidate pool, using a small query set from the target task. Despite growing interest, the literature on targeted instruction selection remains fragmented and opaque: methods vary widely in selection budgets, often omit zero-shot baselines, and frequently entangle the contributions of key components. As a result, practitioners lack actionable guidance on selecting instructions for their target tasks. In this wor
The proliferation of LLMs and growing interest in fine-tuning necessitates clearer guidance on data selection, which this research aims to provide.
Understanding effective instruction selection is crucial for improving LLM performance and efficiency, directly impacting the development and deployment of AI agents and specialized AI applications.
This research provides a clearer framework for practitioners to select instruction data, potentially leading to more robust and less resource-intensive LLM fine-tuning processes.
- · AI developers
- · LLM practitioners
- · AI-powered product companies
- · Inefficient instruction selection methods
- · LLM fine-tuning practices without clear methodology
Improved performance and reliability of specialized large language models.
Faster development cycles and reduced costs for building AI-powered applications, especially agentic systems.
Accelerated adoption of AI across various industries as models become more tailored and effective for specific tasks.
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Read at arXiv cs.LG