2 citations · 5 across the 16 of their papers we have counts for
5 papers · 1 filter
SeDeM: Selective Decompression of Hidden-State Memories for Long-Context Question Answering
Maryam Haghifam, Jason Cong, Yizhou Sun
Long-context inference with large language models (LLMs) is costly: self-attention during prefill scales quadratically with sequence length, and the key-value (KV) cache grows with…
HMT: Semantic Hierarchy-Aware Hierarchical Memory Transformer
Maryam Haghifam, Zifan He, Jason Cong +1
Transformer-based LLMs achieve strong results on many language tasks; however, long inputs remain challenging because context windows are finite, and prefill latency and memory gro…
Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review
Neha Prakriya, Jui-Nan Yen, Cho-Jui Hsieh +1
Traditional Large Language Model (LLM) pretraining relies on autoregressive language modeling with randomly sampled data from web-scale datasets. Inspired by human learning techniq…
Optimized Multi-Token Joint Decoding with Auxiliary Model for LLM Inference
Zongyue Qin, Ziniu Hu, Zifan He +3
Large language models (LLMs) have achieved remarkable success across diverse tasks, yet their inference processes are hindered by substantial time and energy demands due to single-…
HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing
Zifan He, Yingqi Cao, Zongyue Qin +3
Transformer-based large language models (LLM) have been widely used in language processing applications. However, due to the memory constraints of the devices, most of them restric…