565 citations · 889 across the 33 of their papers we have counts for
38 papers
Language Models Can Control Their Own Attention
Namgyu Ho, Huzama Ahmad, Woosung Koh +3
Language models spend most of their attention on a small fraction of context, yet they read the entire KV cache to find the few tokens that matter. If the user asks about a previou…
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…
Fast Inference via Hierarchical Speculative Decoding
Clara Mohri, Haim Kaplan, Tal Schuster +2
Transformer language models generate text autoregressively, making inference latency proportional to the number of tokens generated. Speculative decoding reduces this latency witho…
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…
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…
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?…