Copy-Paste Language Models

Nathan Godey, Yoav Artzi

Copy-Paste Language Models: 10 upvotes on Hugging Face Daily Papers, #53 of 84 papers on 2026-10-16. Day-by-day upvote history.

Transformers are intuitively considered to be efficient at copying from their inputs. We actually show that the ability of LLMs to copy in context can be improved through explicit modeling. We propose Copy-Paste Language Models (CPLMs), which add a lightweight Copy-Paste head to the conventional language modeling (LM) head. CPLMs improve training efficiency, downstream performance, and offer new operating points in long-context and retrieval setups. We validate our approach by pretraining models from 135M to 1.7B parameters, showing improvements across all scales and backbones with negligible training and inference overheads. We explain the success of CPLMs by showing that they partially mitigate the LM head gradient bottleneck through an alternative gradient path.

Paper page on Hugging Face · arXiv

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