arXiv:2510.06198v3 Announce Type: replace Abstract: Despite the remarkable reasoning capabilities of large language models, they still struggle with one-shot relation extraction without predefined relation labels. We identify two pitfalls: models are often misled by irrelevant tokens instead of relation-conveying semantics, and they often fail to align with the abstraction level human annotators expect. We introduce a novel framework that closes this gap with two components: (1) COGRE, a cognitively-inspired reasoning framework that structures RE into a series of processes mimicking human text
The paper identifies critical limitations in current large language models' ability to perform one-shot relation extraction, proposing a novel solution to align AI reasoning more closely with human cognitive processes.
Improving AI's ability to extract relations and provide explainable reasoning is crucial for trust, adoption, and effectiveness in complex analytical tasks, impacting various industries that rely on automated knowledge extraction.
This framework offers a path toward more accurate, less 'black box' AI reasoning, potentially accelerating the development of more reliable AI agents for information processing.
- · AI researchers
- · NLP developers
- · Industries relying on data extraction
- · Developers of AI agents
- · AI models with opaque reasoning
- · Manual data annotation services
Improved accuracy and explainability in AI-driven relation extraction.
Increased adoption of AI agents for complex information processing due to enhanced reliability.
Reduced need for extensive human oversight in AI-driven data analysis, leading to accelerated decision-making cycles across sectors.
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Read at arXiv cs.CL