Most Cited COLM "convolutional layers" Papers
418 papers found • Page 3 of 3
Conference
Scaling Laws of Synthetic Data for Language Model
Zeyu Qin, Qingxiu Dong, Xingxing Zhang et al.
Can Test-Time Scaling Improve World Foundation Model?
Wenyan Cong, Hanqing Zhu, Peihao Wang et al.
SQuat: Subspace-orthogonal KV Cache Quantization
Hao Wang, Ligong Han, Kai Xu et al.
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Bowen Jin, Hansi Zeng, Zhenrui Yue et al.
JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model
Yi Nian, Shenzhe Zhu, Yuehan Qin et al.
Impact-driven Context Filtering For Cross-file Code Completion
Yanzhou Li, Shangqing Liu, Kangjie Chen et al.
Agents Are All You Need for LLM Unlearning
Debdeep Sanyal, Murari Mandal
Plato: Plan to Efficient Decode for Large Language Model Inference
Shuowei Jin, Xueshen Liu, Yongji Wu et al.
Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models
Hyunwoo Kim, Melanie Sclar, Tan Zhi-Xuan et al.
VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information
Ryo Kamoi, Yusen Zhang, Sarkar Snigdha Sarathi Das et al.
Towards User-level Private Reinforcement Learning with Human Feedback
Jiaming Zhang, Mingxi Lei, Meng Ding et al.
Analyzing Multilingualism in Large Language Models with Sparse Autoencoders
Ikhyun Cho, Julia Hockenmaier
IMPersona: Evaluating Individual Level LLM Impersonation
Quan Shi, Carlos E Jimenez, Stephen Dong et al.
Towards Compute-Optimal Many-Shot In-Context Learning
Shahriar Golchin, Yanfei Chen, Rujun Han et al.
CodeXEmbed: A Generalist Embedding Model Family for Multilingual and Multi-task Code Retrieval
Ye Liu, Rui Meng, Shafiq Joty et al.
Evaluating LLMs on Chinese Idiom Translation
Cai Yang, Yao Dou, David Heineman et al.
EvidenceBench: A Benchmark for Extracting Evidence from Biomedical Papers
Jianyou Wang, Weili Cao, Kaicheng Wang et al.
Overfill: Two-Stage Models for Efficient Language Model Decoding
Woojeong Kim, Junxiong Wang, Jing Nathan Yan et al.