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Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
Tomato, Tomahto, Tomate: Do Multilingual Language Models Understand Based on Subword-Level Semantic Concepts?
Crystina Zhang, Jing Lu, Vinh Q. Tran +3
Human understanding of text depends on general semantic concepts of words rather than their superficial forms. To what extent does our human intuition transfer to language models?…
Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation
Sangmin Bae, Yujin Kim, Reza Bayat +8
Scaling language models unlocks impressive capabilities, but the accompanying computational and memory demands make both training and deployment expensive. Existing efficiency effo…
Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA
Sangmin Bae, Adam Fisch, Hrayr Harutyunyan +3
Large language models (LLMs) are expensive to deploy. Parameter sharing offers a possible path towards reducing their size and cost, but its effectiveness in modern LLMs remains fa…
On Reference (In-)Determinacy in Natural Language Inference
Sihao Chen, Chaitanya Malaviya, Alex Fabrikant +4
We revisit the reference determinacy (RD) assumption in the task of natural language inference (NLI), i.e., the premise and hypothesis are assumed to refer to the same context when…
Block Transformer: Global-to-Local Language Modeling for Fast Inference
Namgyu Ho, Sangmin Bae, Taehyeon Kim +6
We introduce the Block Transformer which adopts hierarchical global-to-local modeling to autoregressive transformers to mitigate the inference bottlenecks associated with self-atte…