A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Zheng Lin, Guanqiao Qu, Qiyuan Chen, et al.
This article explores deploying large language models at the 6G edge through end-edge cooperation, discussing architecture, training, inference, and open challenges.
Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, et al.
HuBERT uses offline clustering to provide aligned targets for masked prediction, matching or improving wav2vec 2.0 on speech benchmarks.
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.
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An empirical study showing that fine-tuned judge models are not a general substitute for GPT-4 in LLM evaluation, urging caution in real applications.
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RidgeloRA enhances LoRA by incorporating matrix ridge regression to improve parameter-efficient fine-tuning of large language models.
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The paper introduces nested low-rank adaptation, a method that reduces the number of trainable parameters for fine-tuning large models compared to standard LoRA.
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This study presents a low-rank adaptation method for finetuning large language models, embedding a semantic-aware low-rank adaptation module into attention layers.
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Vision-R1 introduces vision-guided reinforcement learning to achieve human-free alignment in large vision-language models, eliminating the need for preference data.
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Proposes native reasoning models that train language models to reason on unverifiable data, moving beyond SFT and RLVR.
Giorgio Roffo
This tutorial explores advanced LLM techniques including RAG, PAL, ReAct, LangChain, fine-tuning strategies, and transformer architectures to address limitations like temporal cutoffs and mathematical inaccuracies.
Karsten Roth, Vishaal Udandarao, Sebastian Dziadzio, et al.
This paper introduces FoMo-in-Flux, a continual multimodal pretraining benchmark with realistic compute constraints, and provides comprehensive guidance for effective continual model updates in real-world deployment scenarios.
Komal Kumar, Tajamul Ashraf, Omkar Thawakar, et al.
A systematic survey of post-training techniques for LLMs, covering fine-tuning, reinforcement learning, and test-time scaling, with a public repository for tracking developments.