5 papers · 1 filter
Unveiling the Influence of Amplifying Language-Specific Neurons
Inaya Rahmanisa, Lyzander Marciano Andrylie, Mahardika Krisna Ihsani +3
Language-specific neurons in LLMs that strongly correlate with individual languages have been shown to influence model behavior by deactivating them. However, their role in amplifi…
Sparse Autoencoders Can Capture Language-Specific Concepts Across Diverse Languages
Lyzander Marciano Andrylie, Inaya Rahmanisa, Mahardika Krisna Ihsani +3
Understanding the multilingual mechanisms of large language models (LLMs) provides insight into how they process different languages, yet this remains challenging. Existing studies…
IndoSafety: Culturally Grounded Safety for LLMs in Indonesian Languages
Muhammad Falensi Azmi, Muhammad Dehan Al Kautsar, Alfan Farizki Wicaksono +1
Although region-specific large language models (LLMs) are increasingly developed, their safety remains underexplored, particularly in culturally diverse settings like Indonesia, wh…
Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation
Naila Shafirni Hidayat, Muhammad Dehan Al Kautsar, Alfan Farizki Wicaksono +1
The performance of large language models (LLMs) continues to improve, as reflected in rising scores on standard benchmarks. However, the lack of transparency around training data r…
University of Indonesia at SemEval-2025 Task 11: Evaluating State-of-the-Art Encoders for Multi-Label Emotion Detection
Ikhlasul Akmal Hanif, Eryawan Presma Yulianrifat, Jaycent Gunawan Ongris +4
This paper presents our approach for SemEval 2025 Task 11 Track A, focusing on multilabel emotion classification across 28 languages. We explore two main strategies: fully fine-tun…