Automated Instruction Revision (AIR): A Structured Comparison of Task Adaptation Strategies for LLM
Solomiia Bilyk, Volodymyr Getmanskyi, Taras Firman
TLDR
This paper introduces Automated Instruction Revision (AIR) for LLM adaptation, comparing it with other methods and showing task-dependent performance.
Key contributions
- Introduces Automated Instruction Revision (AIR) for LLM adaptation using limited examples.
- Compares AIR with prompt optimization, retrieval, and fine-tuning across diverse benchmarks.
- Demonstrates that LLM adaptation performance is strongly task-dependent, with no single dominant method.
- Shows AIR excels in label-remapping, while retrieval and fine-tuning suit other specific tasks.
Why it matters
This paper offers a structured comparison of LLM adaptation strategies, revealing that no single method is universally superior. It provides crucial insights into when to use Automated Instruction Revision (AIR) versus retrieval or fine-tuning, guiding practitioners in selecting optimal approaches for specific tasks.
Original Abstract
This paper studies Automated Instruction Revision (AIR), a rule-induction-based method for adapting large language models (LLMs) to downstream tasks using limited task-specific examples. We position AIR within the broader landscape of adaptation strategies, including prompt optimization, retrieval-based methods, and fine-tuning. We then compare these approaches across a diverse benchmark suite designed to stress different task requirements, such as knowledge injection, structured extraction, label remapping, and logical reasoning. The paper argues that adaptation performance is strongly task-dependent: no single method dominates across all settings. Across five benchmarks, AIR was strongest or near-best on label-remapping classification, while KNN retrieval performed best on closed-book QA, and fine-tuning dominated structured extraction and event-order reasoning. AIR is most promising when task behavior can be captured by compact, interpretable instruction rules, while retrieval and fine-tuning remain stronger in tasks dominated by source-specific knowledge or dataset-specific annotation regularities.
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