arXiv:2606.18933v1 Announce Type: new Abstract: Active feature acquisition (AFA) sequentially selects which features to observe to reach a classification or ranking decision. Its central limitation is reliance on large amount of labeled data to fit probabilistic models guiding acquisition. Large language models (LLMs) supply unsupervised domain knowledge, but are poor sequential planners. Asking one to both know and decide conflates capabilities best kept separate. Here, we develop a framework for zero-shot AFA through disciplined elicitation: asking the LLM only for what it can be trusted to
The proliferation of powerful LLMs and the recognition of their limitations in sequential planning are driving new research to integrate their unsupervised domain knowledge more effectively.
This development proposes a method for more efficient data acquisition in AI systems, potentially reducing reliance on large labeled datasets and accelerating model development.
The approach separates the LLM's knowledge elicitation from its decision-making, offering a new paradigm for active feature acquisition that is less data-intensive.
- · AI researchers and developers
- · Companies with limited labeled datasets
- · Sectors requiring efficient data acquisition
- · Traditional active learning methods
- · Data labeling services (potentially long-term)
AI models could be trained more rapidly and cost-effectively, especially in niche domains.
This could lead to a broader adoption of AI in areas where data annotation is a significant bottleneck.
The reduced need for labeled data might shift investment from data collection to advanced model integration and elicitation techniques.
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Read at arXiv cs.LG