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arXiv:2610.07032v1 Announce Type: cross Abstract: Large-scale pretrained transformer models have achieved state-of-the-art performance across diverse machine translation tasks, including multilingual...
arXiv:2606. 31796v1 Announce Type: cross Abstract: We study three complementary techniques for training compute-efficient language models.
arXiv:2603. 01875v3 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is an essential technique to compress large language models (LLMs) into smaller ones.
arXiv:2412. 01282v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) bring powerful understanding and reasoning capabilities to multimodal tasks.
The paper introduces ResMem, a method that transforms retrieved knowledge into a parametric memory for large language models. By using the LLM as an offline teacher that reads retrieved text, ResMem learns an additive logit residual, avoiding large token storage and improving question‑answering performance. Experiments on Wikipedia and five QA datasets show significant storage savings and higher token F1 scores compared to k‑NN baselines.
arXiv:2510. 20535v2 Announce Type: replace-cross Abstract: Recent techniques such as retrieval-augmented generation or chain-of-thought reasoning have led to longer contexts and increased inference costs.