13 citations · 37 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
Self-Training Elicits Concise Reasoning in Large Language Models
Tergel Munkhbat, Namgyu Ho, Seo Hyun Kim +3
Chain-of-thought (CoT) reasoning has enabled large language models (LLMs) to utilize additional computation through intermediate tokens to solve complex tasks. However, we posit th…
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models
Seungone Kim, Juyoung Suk, Ji Yong Cho +29
As language models (LMs) become capable of handling a wide range of tasks, their evaluation is becoming as challenging as their development. Most generation benchmarks currently as…
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…
HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning
Yongjin Yang, Joonkee Kim, Yujin Kim +3
With the proliferation of social media, accurate detection of hate speech has become critical to ensure safety online. To combat nuanced forms of hate speech, it is important to id…
Large Language Models Are Reasoning Teachers
Namgyu Ho, Laura Schmid, Se-Young Yun
Recent works have shown that chain-of-thought (CoT) prompting can elicit language models to solve complex reasoning tasks, step-by-step. However, prompt-based CoT methods are depen…