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:2608.29459v1 Announce Type: new
Abstract: Skills, as a useful abstraction for the procedural capabilities of large language models (LLMs), capture how models perform structured, multi-step reas...
By Xunyi Jiang, Junda Wu, Yuxin Xiong, Sheldon Yu, Tong Yu, David Arbour, Ritwik Sinha, Julian McAuley, Hongyi Wen
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
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:2607. 08399v1 Announce Type: cross Abstract: Large language models process prompts by propagating activations through dozens of layers before generating a response.
By Thibaud Ardoin, Semira Einsele, Evis Bregu, Gerhard Wunder
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
arXiv:2606. 17164v1 Announce Type: cross Abstract: Prompting has become the primary interface between humans and generative AI, yet many natural language prompts remain fragile: roles, goals, constraints, and expected outputs are often buried in prose or left implicit.
By Enkhzol Dovdon
arXiv:2208. 00859v2 Announce Type: replace Abstract: We propose a novel method enabling autocompletion of chemical flowsheets.
By Gabriel Vogel, Lukas Schulze Balhorn, Artur M. Schweidtmann
Language models are typically pretrained from random initialization. Recent work challenges this convention, showing that a brief warm-up on abstract, algorithmically generated data can provide a bett...
Instruction tuning is often thought to give language models a universal ability to follow instructions, but this study shows otherwise. By probing nine tasks across three models, the authors find that general probes reveal selective, not uniform, deficits, cross‑task transfer is weak and skill‑similar, and causal ablation uncovers sparse, asymmetric dependencies. The results suggest instruction following is a coordinated use of diverse linguistic skills rather than a single shared mechanism.
By Elisabetta Rocchetti, Alfio Ferrara
arXiv:2605. 27642v2 Announce Type: replace-cross Abstract: Soft prompting, also known as continuous prompting, is a parameter-efficient method for tuning LLMs to specific tasks.
By Pitipat Kongsomjit, Suryansh Goyal, Jacob Whitehill
Large Language Models: A Plain-Language Overview for Engineers explains that LLMs are neural networks trained on vast text collections to predict subsequent words, and outlines key concepts such as Transformers, pre‑training, fine‑tuning, reinforcement learning from human feedback, retrieval‑augmented generation, chain‑of‑thought prompting, and tool‑using agents. It also discusses efficiency techniques like FlashAttention and QLoRA, provides a life‑cycle figure and comparative tables, and offers a checklist for evaluating LLM claims, including checks for data contamination, model‑based grading, long‑context handling, and hallucination. The article aims to give engineers a clear picture of how training changes and prompts affect generative AI tools like ChatGPT.
By Bahaeddin T\"urko\u{g}lu