A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Zhen Tan, Dawei Li, Song Wang, et al.
This survey uniquely focuses on LLMs for data annotation, covering generation, assessment, and utilization of LLM-generated annotations.
P. Colombo, T. Pires, Malik Boudiaf, et al.
SaulLM-7B is a 7-billion-parameter LLM tailored for the legal domain, trained on over 30 billion tokens of English legal text and instruction-tuned to achieve state-of-the-art legal comprehension and generation.
Subbarao Kambhampati
Argues that LLMs lack genuine reasoning and planning capabilities, despite their impressive language generation, and that their apparent success is due to memorization and pattern matching.
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, et al.
Code Llama is a family of open-source LLMs for code based on Llama 2, achieving state-of-the-art performance in code generation, infilling, and long-context tasks.
James B. Grace, Donald R. Schoolmaster, Glenn R. Guntenspergen, et al.
This paper presents guidelines for a third-generation structural equation modeling framework based on graph theory, causal graphs, and probabilistic reasoning.
Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi, et al.
A comprehensive survey of deep learning-based image captioning, covering visual encoding, text generation, training strategies, datasets, and evaluation metrics.
Krzysztof Czarnecki, Ulrich W. Eisenecker
This book introduces generative programming, a paradigm for automating software component assembly using domain engineering, feature modeling, and code generation.
Soren Kejser Jensen, Torben Bach Pedersen, Christian Thomsen
A survey classifying Time Series Management Systems by architecture, storage, querying, stream processing, and approximate query processing, with a vision for next-generation systems.
Zackary Rackauckas
Evaluates RAG-Fusion combining RAG and reciprocal rank fusion for product information retrieval, finding accurate answers but occasional off-topic responses.
Zongxi Li, Zijian Wang, Weiming Wang, et al.
A systematic survey of Retrieval-Augmented Generation (RAG) in education, covering workflow, retrievers, generation optimization, and applications.
Jakub Swacha, Michał Gracel
A survey of 47 papers on RAG chatbots in education, analyzing their character, target support, knowledge scope, LLM, and evaluation.
Paul C. D. Hawkins, A. Geoffrey Skillman, Gregory L. Warren, et al.
OMEGA is a systematic, knowledge-based conformer generator validated against high-quality PDB and CSD structures, showing strong performance in reproducing crystallographic conformations.