Papers with annotation purposes

2 papers
IMGTB: A Framework for Machine-Generated Text Detection Benchmarking (2024.acl-demos)

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Challenge: MGTD methods are needed in many areas, such as prevention of disinformation spreading, plagiarism, impersonation and identity theft.
Approach: They propose a framework for machine-generated text detection that integrates custom methods and evaluation datasets into existing frameworks.
Outcome: The proposed framework simplifies the benchmarking of machine-generated text detection methods by easy integration of custom (new) methods and evaluation datasets.
Label and Explanation Variation in LLM-Based Annotation: a Case Study in Natural Language Inference (2026.acl-long)

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Challenge: Large language models (LLMs) have shown considerable promise for annotation purposes, but questions remain about their ability to capture human label variation (HLV) label variation is genuine disagreement between annotators observed across NLP tasks.
Approach: They investigate how label and explanation variation manifests within and across LLMs with respect to the Natural Language Inference task.
Outcome: The proposed models generate label distributions similar to humans but exhibit distinct, idiosyncratic judgments and disagreement patterns.

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