Papers with fully trained model
How a Bilingual LM Becomes Bilingual: Tracing Internal Representations with Sparse Autoencoders (2025.findings-emnlp)
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Tatsuro Inaba, Go Kamoda, Kentaro Inui, Masaru Isonuma, Yusuke Miyao, Yohei Oseki, Yu Takagi, Benjamin Heinzerling
| Challenge: | Using sparse autoencoders, we explore how bilingual language models develop complex internal representations. |
| Approach: | They employ sparse autoencoders to analyze bilingual language models' internal representations. |
| Outcome: | The proposed method integrates decomposed representations from a fully trained model into a mid-training model. |
FLOP-Efficient Training: Early Stopping Based on Test-Time Compute Awareness (2026.findings-acl)
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| Challenge: | Prior work shows that increasing test-time compute (TTC) can improve accuracy of large language models. |
| Approach: | They propose a TTC-aware training algorithm that jointly selects a checkpoint and a corresponding TTC configuration to minimize training compute without sacrificing accuracy. |
| Outcome: | The proposed method reduces training compute by 92% while maintaining accuracy. |