A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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.
Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh, et al.
Proposes Lookback Lens, a simple hallucination detector using attention weight ratios, effective across tasks and models, and reduces hallucinations via classifier-guided decoding.
Zhuowan Li, Cheng Li, Mingyang Zhang, et al.
Compares RAG and long-context LLMs, finding long-context outperforms RAG but at higher cost, and proposes Self-Route to route queries based on model self-reflection.
Eric J. Bigelow, Daniel Wurgaft, YingQiao Wang, et al.
A unified Bayesian model shows that activation steering and in-context learning control LLMs by altering concept priors and accumulating evidence, respectively.
Rachit Bansal, Aston Zhang, Rishabh Tiwari, et al.
Proposes test-time training via targeted gradient updates on context to overcome static self-attention limitations in long-context LLMs, outperforming inference-time scaling.
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.
William Stallings
A comprehensive textbook survey of cryptography and network security principles and practice, covering both foundational concepts and modern applications.
Fabrizio Sebastiani
This survey reviews machine learning approaches to automated text categorization, covering document representation, classifier construction, and evaluation.
Ingo Feinerer, Kurt Hornik, David Meyer
The paper introduces the tm package for R, providing a comprehensive framework for text mining applications including preprocessing, analysis, and machine learning.
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.
Mohsin Munir, Shoaib Ahmed Siddiqui, Andreas Dengel, et al.
DeepAnT uses a CNN-based predictor and unsupervised anomaly detector to detect point, contextual, and discord anomalies in time series without requiring labeled data.
Dan Gusfield
A comprehensive textbook on string algorithms, suffix trees, and sequence alignment with applications in computational biology.