A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.
A practical guide for practitioners on using LLMs in NLP tasks, covering models, data, use/non-use cases, biases, and deployment considerations.
Jianghao Lin, Xinyi Dai, Yunjia Xi, et al.
A comprehensive survey of how large language models can be adapted to recommender systems, organized by where LLMs fit in the pipeline and how they are trained and used.
Yagmur Yigit, Mohamed Amine Ferrag, Mohamed Chahine Ghanem, et al.
A review of AI-driven approaches for critical infrastructure protection, covering LLM cybersecurity benchmarks, trust and privacy issues, and agentic AI for proactive defense.
Mingjin Zhang, Xiaoming Shen, Jiannong Cao, et al.
EdgeShard partitions LLMs into shards across collaborative edge devices and cloud, using dynamic programming to optimize latency and throughput without accuracy loss.
Yuji Cao, Huan Qi Zhao, Yuheng Cheng, et al.
This survey proposes a taxonomy of four roles for LLMs in reinforcement learning—information processor, reward designer, decision-maker, and generator—and reviews methods, challenges, and future directions.
Juyong Jiang, Fan Wang, Jiasi Shen, et al.
A systematic survey of LLMs for code generation, covering data curation, advances, evaluation, ethics, environmental impact, and applications, with comparative analysis on HumanEval, MBPP, and BigCodeBench.
B.‐C. CHEN, Zhaofeng Zhang, Nicolas Langrené, et al.
This review surveys foundational and advanced prompt engineering techniques for LLMs and VLMs, evaluates them with subjective and objective metrics, and examines prompt engineering's role in AI security.
John M. Dagdelen, Alexander Dunn, Sang‐Hoon Lee, et al.
Fine-tuning pretrained LLMs (GPT-3, Llama-2) enables joint named entity recognition and relation extraction from scientific text, producing structured records for materials chemistry.
Shirui Pan, Linhao Luo, Yufei Wang, et al.
This paper presents a roadmap for unifying large language models and knowledge graphs through three frameworks: KG-enhanced LLMs, LLM-augmented KGs, and synergized LLMs+KGs.
Unknown
The paper presents methods for efficient and effective uncertainty quantification in large language models, aiming to provide scalar estimates of answer correctness.
Unknown
LLM-Evolve introduces a multi-round evaluation framework to assess how LLMs' capabilities evolve across repeated benchmark interactions.
Unknown
An empirical study showing how text quality interventions reshape neural scaling laws for LLMs, affecting performance projection and resource planning.