arXiv:2606.18620v1 Announce Type: cross Abstract: Existing information extraction (IE) tasks increasingly adopt in-context learning (ICL) with large language models. However, current approaches either show inconsistent performance across model scales or lack systematic optimization and generalizability. Building on this, we propose BCL (Bayesian In-Context Learning Framework for Information Extraction), the first optimization framework that uses particle filtering with Bayesian updates to systematically refine label representations across IE tasks. Through four steps initialization, observatio
The proliferation of ICL with large language models has exposed inconsistencies, driving the need for more systematic and generalizable optimization frameworks like BCL.
This development addresses a key limitation in current AI applications, suggesting a more robust approach to information extraction which is critical for agentic systems and data processing.
The introduction of BCL marks a shift towards more systematic and optimized in-context learning, potentially leading to more reliable and generalizable AI performance across various IE tasks.
- · AI developers
- · Data analysis platforms
- · Enterprise AI integration
- · Ad-hoc ICL methods
- · Manual data extraction processes
Improved accuracy and efficiency in information extraction tasks.
Accelerated development and deployment of sophisticated AI agents reliant on high-quality data input.
Enhanced automation of complex white-collar tasks, impacting labor markets and operational costs.
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Read at arXiv cs.AI