Large language models with in-context learning outperform fine-tuned BERT and RoBERTa for music entity detection in user-generated content, but their edge shrinks for entities not memorized during pre-training.
Multilingual Abusiveness Identification on Code-Mixed Social Media Text
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abstract
Social Media platforms have been seeing adoption and growth in their usage over time. This growth has been further accelerated with the lockdown in the past year when people's interaction, conversation, and expression were limited physically. It is becoming increasingly important to keep the platform safe from abusive content for better user experience. Much work has been done on English social media content but text analysis on non-English social media is relatively underexplored. Non-English social media content have the additional challenges of code-mixing, transliteration and using different scripture in same sentence. In this work, we propose an approach for abusiveness identification on the multilingual Moj dataset which comprises of Indic languages. Our approach tackles the common challenges of non-English social media content and can be extended to other languages as well.
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2024 1verdicts
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A Benchmark and Robustness Study of In-Context-Learning with Large Language Models in Music Entity Detection
Large language models with in-context learning outperform fine-tuned BERT and RoBERTa for music entity detection in user-generated content, but their edge shrinks for entities not memorized during pre-training.