arXiv:2607. 18305v1 Announce Type: cross Abstract: Some limits on what language models know are not gaps in data coverage but structural properties of learning from text.
By Priyansh Srivastava, Romit Chatterjee
arXiv:2607. 04223v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) reduces but does not eliminate hallucination, and existing detectors return a single answer-level score that does not indicate which sentence is unsupported, or why.
By Mohamed Aly Bouke
arXiv:2607. 11020v1 Announce Type: cross Abstract: Continual learning promises a language model that keeps acquiring knowledge after training, with each new fact written into its weights.
By Charles O'Neill
arXiv:2605.10795v2 Announce Type: replace-cross
Abstract: Large language models excel at factual recall, yet the fundamental limits of storing input--output associations with neural networks remain u...
By Alessio Giorlandino, Sebastian Goldt, Antoine Maillard
arXiv:2607. 12649v1 Announce Type: new Abstract: Recent work on extractable memorization in LLMs suffers from two contrasting validity problems.
By A. Feder Cooper, Marika Swanberg, Jamie Hayes, Lea Duesterwald, Christopher De Sa, Daniel E. Ho, Mark A. Lemley, Percy Liang
The paper presents a systematic study of how different compression techniques—pruning, quantization, and distillation—affect the capabilities of large language models (LLMs) in tasks such as mathematics, code generation, and question answering. It introduces a framework that measures capability loss and relates it to factors like model size, training stage, and compression settings, yielding simple predictive relations that generalize across unseen configurations. The authors demonstrate that sharing density responses across pruning levels can dramatically reduce the number of measurements needed, and that their predictive models closely match regression results while offering efficient decision guidance for compression method selection.
By Xueqi Cheng, Liang Wu, Kelly Wan, Liangjie Hong, Yushun Dong