AI Research
Experts Rise Where LLMs Disagree: Using Cross-Model Disagreement to Target Expert Effort in LLM Codebook Revision for Large-Scale Annotation
Leveraging LLM disagreement to streamline expert-driven annotation and codebook development.
Large-scale text annotation brings expert insight to millions of documents, often through a codebook that AI annotators follow. Developing a robust codebook, however, takes months. Large language models (LLMs) could speed this process by applying an early codebook to the data, su...
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ArXiv CS.CL
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ArXiv CS.CL
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By leveraging cross-model disagreement, researchers can automatically pinpoint ambiguous areas in annotation guidelines that require human intervention. This approach significantly reduces the manual labor required for iterative codebook refinement, making large-scale data labeling more efficient and accurate.
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