Hugging Face Trending Papers
Oct 29

Grammar Control for Language Learning with Sequential Monte Carlo and Small Language Models

The paper presents a method that uses sequential Monte Carlo (SMC) to guide small language models toward generating specific target grammar structures for second language learners. By applying a minimal set of hand‑coded potential functions, SMC achieves near‑perfect coverage of the desired grammar, allowing these lightweight models to outperform state‑of‑the‑art models that are 140 times larger. The authors also demonstrate that code synthesis with frontier models can automatically generate new potentials, enabling the approach to scale to hundreds of grammar skills without additional training or supervised data.

arXiv AI
Sep 3

DKL: Decoupled Knowledge Learning for Instruction-Tuned Language Models

The paper introduces DKL, a method for adding new knowledge to instruction‑tuned language models without compromising their instruction‑following abilities. DKL performs extended pre‑training on a base LLM to embed knowledge, then merges these weights into the instruction‑tuned model, avoiding costly instruction fine‑tuning. Experiments show DKL raises RAG accuracy from 54.17% to 79.26% on retrieval failure cases while using far less training data than previous approaches.

By Kushagra Bhushan, Meghanadh Pulivarthi, Sai Krishna Reddy Sathi, Gaurav Pandey, Sonam Gupta, Vineet Kumar, Jaydeep Sen, Yatin Nandwani, Sachindra Joshi, Dinesh Raghu
Hugging Face Trending Papers
Sep 2

DKL: Decoupled Knowledge Learning for Instruction-Tuned Language Models

The paper introduces DKL, a method that decouples knowledge learning from instruction tuning in language models. Instead of fine‑tuning the instruction‑tuned model directly, DKL first extends pre‑training on a base model to embed new knowledge, then merges these weights into the instruction‑tuned model, preserving its instruction‑following abilities. Experiments show DKL raises RAG accuracy from 54.17 % to 79.26 % on retrieval failures, outperforming prior methods while using far less training data.

arXiv AI
Sep 25

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.

By Tong Xiao, Jingbo Zhu