arXiv:2507. 02259v2 Announce Type: replace-cross Abstract: Despite improvements by length extrapolation, efficient attention and memory modules, handling infinitely long documents with linear complexity without performance degradation during extrapolation remains the ultimate challenge in long-text processing.

By Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei-Ying Ma, Jingjing Liu, Mingxuan Wang, Hao Zhou

llms agents reinforcement-learning

Hugging Face Blog

Jul 17, 2023

Open-Source Text Generation & LLM Ecosystem at Hugging Face

llms

Hugging Face Trending Papers

Jun 29

Little Brains, Big Feats: Exploring Compact Language Models

While large language models have been dominating the research landscape recently, small language models remain highly relevant across various domains; yet, they receive far less attention. In this study, we investigate how smaller language models perform during the generation stage within a Retrieval-Augmented Generation (RAG) system.

llms rag benchmarks

arXiv AI

Jul 7

Na\"ive PAINE: Lightweight Text-to-Image Generation Improvement with Prompt Evaluation

arXiv:2603. 12506v2 Announce Type: replace-cross Abstract: Text-to-Image (T2I) generation is primarily driven by Diffusion Models (DM) which rely on random Gaussian noise.

By Joong Ho Kim, Nicholas Thai, Souhardya Saha Dip, Dong Lao, Keith G. Mills

diffusion benchmarks

arXiv AI

Jul 29

The Effect of Text Chunk Size on Retrieval-Augmented Generation Performance

arXiv:2607. 24767v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems have emerged as a powerful process for allowing large language models (LLMs) to retrieve relevant information to use as source material during text generation.

By German Garrido-Lestache Belinchon, Hugo Garrido-Lestache Belinchon

llms rag computer-vision

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