The paper surveys Transformer-based large language models (LLMs) with a focus on efficiency, reviewing 312 articles that cover data curation, model design, downsizing, and dynamic inference. It also examines efficiency in adaptation strategies such as pre‑training, fine‑tuning, prompt‑engineering, and Retrieval‑Augmented Generation (RAG). A statistical analysis and evaluation of over 30 prominent NLP models on 13 benchmarks provide insights into both efficiency and efficacy, highlighting trends toward sustainable NLP practices.
By Wazib Ansar, Saptarsi Goswami, Amlan Chakrabarti
arXiv:2606. 23533v2 Announce Type: replace Abstract: Recent large language models (LLMs) are good at general text generation, but it is still hard to use them for domain-specific data generation because the output must follow strict formatting and structural rules.
By Hung Phan, Aniroop Naladala, Dubey Avanindra, Supryia Chinthavali, Lunga Dalton, Ali Jannesari
The paper analyzes the environmental footprint of machine learning model training, focusing on large language models and their hardware. It finds that energy use and environmental impacts have risen exponentially over the past decade, even when employing carbon‑efficient electricity and more efficient hardware. The study argues that optimization strategies alone cannot curb these impacts due to a rebound effect, and stresses the need to evaluate hardware life‑cycle impacts and integrate environmental metrics into NLP research practices.
By Cl\'ement Morand (STL), Anne-Laure Ligozat (ENSIIE, LISN, STL), Aur\'elie N\'ev\'eol (STL, LISN)
The article reviews how Large Models (LMs) based on Transformer architectures and self‑supervised pre‑training can address longstanding challenges in Battery Prognostics and Health Management (BPHM). It surveys LM applications across data scarcity, generalization, interpretability, and system automation, and outlines a roadmap for future research, including collaborative data ecosystems, validation, trustworthiness, and efficient deployment. The review aims to guide researchers and practitioners in developing next‑generation battery management systems that are safe, reliable, and autonomous throughout battery lifecycles.
By Jiale Liu, Huan Wang, Weicheng Wang, Rong Zhu, Qiqi Wang, Min Xie
arXiv:2607. 09762v1 Announce Type: new Abstract: Public battery aging datasets are a critical asset for advanced health management, but their practical use is often limited by inconsistent formats, unclear schemas, and metadata scattered across repositories and publications.
By Tianwen Zhu, Hao Wang, Yonggang Wen
Sera is a framework that augments traditional temporal models for battery state‑of‑health forecasting with semantic representations of degradation. It extracts degradation semantics from time‑series data using rule‑based knowledge and large‑language‑model interpretation, then encodes and gates these representations with the temporal model’s output. Experiments on a benchmark dataset show that Sera reduces prediction error by up to 37.3% and improves generalizability, while counterfactual analysis confirms that the model’s responses align with the meanings of key degradation descriptors.
By Jiawei Li, Fang Liu, Wei Zhang, Zuming Liu, Man-Fai Ng, Zhi Wei Seh