Lngram v2: Latent N-Gram Memory with Interpretable Discrete Representations

arXiv:2609.03426 · cs.CL · Submitted 2026-09-03 · Read on arXiv

cs.CL

Submitted: 2026-09-03

Updated: 2026-09-22

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 18× in this paper · explained in 2 episodes
• Transformer — 12× in this paper · explained in 2 episodes
• Decode — 6× in this paper · explained in 2 episodes
• Parameter Efficiency — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Product Key Memory — 3× in this paper
• Adaptation — 2× in this paper · explained in 1 episode
• Generative AI — 2× in this paper · explained in 2 episodes
• Hamming Distance — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Straight-Through Estimator — 1× in this paper
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
• Understanding intermediate layers using linear classifier probes
• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
• PaliGemma: A versatile 3B VLM for transfer
• Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models
• DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
• Kwai Keye-VL-2.0 Technical Report
• The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
• GLU Variants Improve Transformer

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