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
arXiv:2610.11567v3 Announce Type: replace-cross
Abstract: Battery state of health (SoH) forecasting is important for battery management, but remains challenging due to nonlinear degradation and heter...
By Jiawei Li, Fang Liu, Wei Zhang, Zuming Liu, Man-Fai Ng, Zhi Wei Seh
arXiv:2606. 26857v1 Announce Type: new Abstract: The interpretation phase of life cycle assessment often lacks structured mechanisms for translating quantified improvement opportunities addressing environmental hotspots into actionable strategic pathways under technological, social, and policy uncertainty.
By Georgios Tsironis, Juan D. Medrano-Garcia, Gonzalo Guillen-Gosalbez
arXiv:2610.19291v1 Announce Type: new
Abstract: In so-called "Industry 4.0," integrated sensors, analytics tools, and other digital technologies supplement industrial workflows, potentiating enhanced...
By Lucas H. McCabe, Sarah Lukens, H. Howie Huang
In so-called "Industry 4.0," integrated sensors, analytics tools, and other digital technologies supplement industrial workflows, potentiating enhanced visibility and automation. Prognostics and healt...
The paper presents a methodological framework that links the FINALES experiment orchestration system with the Kadi4Mat research data management ecosystem to create interoperable, automated workflows for battery research. By coordinating experiment planning, execution, data handling, and analysis across distributed sites, the framework enables reproducible, end‑to‑end experimental pipelines. The authors demonstrate its utility by studying sodium‑ion coin cell formation, using a Gaussian process model to map formation parameters to electrochemical performance and identify promising experimental regions.
By Giovanna Tosato (Karlsruhe Institute of Technology), Leon Merker (Karlsruhe Institute of Technology, Helmholtz Institute Ulm, Technical University of Munich), Monika Vogler (Technical University of Munich), Michael Selzer (Karlsruhe Institute of Technology), Arnd Koeppe (Karlsruhe Institute of Technology)
arXiv:2607. 11459v1 Announce Type: cross Abstract: This paper presents the mAIEnergy dataset, an open-access, multimodal corpus developed to support Large Language Model (LLM) applications in the energy sector.
By Costas Mylonas, Magda Foti