arXiv:2607.02423v1 Announce Type: new Abstract: Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable unlabeled samples for annotation and use as few-shot demonstrations, effectively reducing human annotation costs while promoting high performance. However, existing methods typically rely on output-level signals for sample identification, such as predictive entropy or semantic similarities with test-time data based on external embeddings, which often overlook models' internal dynamics, which could pinpoint specific knowledge gaps. To bridge this ga
The proliferation of LLMs creates an urgent need for more efficient and cost-effective methods to adapt them to specialized domains, driving innovation in active learning techniques.
This development allows for significantly more efficient and targeted training of LLMs, reducing the human and computational resources required for specialized applications and accelerating AI deployment across industries.
The focus of active learning shifts from output-level signals to internal model dynamics, enabling more precise identification of knowledge gaps and optimizing the few-shot learning process.
- · AI developers
- · Enterprises adopting LLMs
- · Specialized AI applications
- · AI model annotators (increased efficiency)
- · Companies relying on inefficient human annotation
- · Generic LLM training methods
Reduced cost and time for fine-tuning LLMs for specific tasks.
Accelerated deployment of highly specialized AI applications across various sectors.
Enhanced competition in niche AI markets due to lower barriers to entry for custom LLMs.
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Read at arXiv cs.LG