WhoSaidIt: Human-LLM Collaborative Annotation for Text-Based Multilingual Speaker-Attribute Classification
arXiv:2605.26070v1 Announce Type: new Abstract: Annotating speaker attributes from text is inherently ambiguous, particularly in multilingual settings where demographic and social cues are implicit and culturally variable. We propose a human-large language model (LLM) collaborative re-annotation framework for stabilizing multilingual speaker-attribute labels under practical resource constraints. Starting from a noisy corpus, we use LLMs to surface recurring annotation rationales through iterative interaction with experts, and apply disagreement-focused sampling for targeted re-annotation. Usin
The proliferation of Large Language Models (LLMs) and the increasing need for high-quality, culturally nuanced data in multilingual settings are driving the exploration of collaborative annotation frameworks.
Improving the accuracy and reliability of speaker-attribute classification in multilingual text is crucial for developing robust, fair, and globally applicable AI systems, especially for personalization, content moderation, and social analytics.
This collaborative framework changes the approach to data annotation from purely human or purely automated to a hybrid model, potentially reducing costs and improving data quality for complex tasks.
- · AI developers
- · Multilingual data platforms
- · Social media analytics companies
- · Researchers in computational linguistics
- · Companies relying solely on traditional manual annotation
- · Low-quality crowdsourcing platforms
More accurate and efficient annotation of complex linguistic data, especially in non-English contexts.
Accelerated development of AI models that can better understand and process culturally specific nuances in natural language.
Enhanced global reach and fairness of AI applications by mitigating biases introduced by poor or culturally insensitive training data.
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