1 citations · 2 across the 24 of their papers we have counts for
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Early Exit Is a Natural Capability in Transformer-based Models: An Empirical Study on Early Exit without Joint Optimization
Weiqiao Shan, Long Meng, Tong Zheng +5
Large language models (LLMs) exhibit exceptional performance across various downstream tasks. However, they encounter limitations due to slow inference speeds stemming from their e…
Forgetting Curve: A Reliable Method for Evaluating Memorization Capability for Long-context Models
Xinyu Liu, Runsong Zhao, Pengcheng Huang +5
Numerous recent works target to extend effective context length for language models and various methods, tasks and benchmarks exist to measure model's effective memorization length…
NDP: Next Distribution Prediction as a More Broad Target
Junhao Ruan, Abudukeyumu Abudula, Xinyu Liu +7
Large language models (LLMs) trained on next-token prediction (NTP) paradigm have demonstrated powerful capabilities. However, the existing NTP paradigm contains several limitation…
Translate-and-Revise: Boosting Large Language Models for Constrained Translation
Pengcheng Huang, Yongyu Mu, Yuzhang Wu +4
Imposing constraints on machine translation systems presents a challenging issue because these systems are not trained to make use of constraints in generating adequate, fluent tra…
Hybrid Alignment Training for Large Language Models
Chenglong Wang, Hang Zhou, Kaiyan Chang +5
Alignment training is crucial for enabling large language models (LLMs) to cater to human intentions and preferences. It is typically performed based on two stages with different o…
Revealing the Parallel Multilingual Learning within Large Language Models
Yongyu Mu, Peinan Feng, Zhiquan Cao +8
In this study, we reveal an in-context learning (ICL) capability of multilingual large language models (LLMs): by translating the input to several languages, we provide Parallel In…