Efficient training of language models to fill in the middle

Very Large Language Models and How to Evaluate Them

Foundations of Large Language Models

Foundations of Large Language Models is a book that focuses on core concepts of large language models rather than exhaustive coverage of the latest technologies. It is organized into six chapters covering pre‑training, generative models, prompting, alignment, inference, and reasoning. The book targets college students, professionals, and practitioners in NLP and related fields, serving as a reference for anyone interested in large language models.

Evaluating large language models trained on code

Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models

The paper investigates multilingual confidence calibration in large language models, revealing that non‑English languages are systematically less well calibrated than English. By analyzing internal representations, the authors find that late‑intermediate layers provide a more reliable confidence signal than the final layer, which is biased by English‑centric training. They propose training‑free methods such as Language‑Aware Confidence Ensemble (LACE) to adaptively select optimal layers per language, aiming to improve global equity and trustworthiness of LLMs.

Understanding and Accelerating the Training of Masked Diffusion Language Models

arXiv:2605. 13026v2 Announce Type: replace-cross Abstract: Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models (ARMs) for language modeling.

Cosmopedia: how to create large-scale synthetic data for pre-training Large Language Models