Daily Paper Cast

🤗 Daily Paper Upvotes: 14 Authors: Shanghaoran Quan, Tianyi Tang, Bowen Yu, An Yang, Dayiheng Liu, Bofei Gao, Jianhong Tu, Yichang Zhang, Jingren Zhou, Junyang Lin Categories: cs.CL Arxiv: http://arxiv.org/abs/2410.23933v1 Title: Language Models can Self-Lengthen to Generate Long Texts Abstract: Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to process long contexts, yet a notable gap remains in generating long, aligned outputs. This

🤗 Daily Paper Upvotes: 12 Authors: Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li Categories: cs.CL, cs.AI Arxiv: http://arxiv.org/abs/2410.24175v1 Title: Constraint Back-translation Improves Complex Instruction Following of Large Language Models Abstract: Large language models (LLMs) struggle to follow instructions with complex constraints in format, length, etc. Following the conventional instruction-tuning practice, previous works conduct post-training on complex

🤗 Daily Paper Upvotes: 11 Authors: Xinghao Wang, Pengyu Wang, Bo Wang, Dong Zhang, Yunhua Zhou, Xipeng Qiu Categories: cs.CL, cs.AI, cs.CV, cs.LG Arxiv: http://arxiv.org/abs/2410.23918v1 Title: BitStack: Fine-Grained Size Control for Compressed Large Language Models in Variable Memory Environments Abstract: Large language models (LLMs) have revolutionized numerous applications, yet their deployment remains challenged by memory constraints on local devices. While scaling laws have

🤗 Daily Paper Upvotes: 11 Authors: Yuxiang Wei, Federico Cassano, Jiawei Liu, Yifeng Ding, Naman Jain, Zachary Mueller, Harm de Vries, Leandro von Werra, Arjun Guha, Lingming Zhang Categories: cs.CL, cs.LG, cs.SE Arxiv: http://arxiv.org/abs/2410.24198v1 Title: SelfCodeAlign: Self-Alignment for Code Generation Abstract: Instruction tuning is a supervised fine-tuning approach that significantly improves the ability of large language models (LLMs) to follow human instructions. We propose

🤗 Daily Paper Upvotes: 10 Authors: Xueyang Yu, Xinlei Chen, Yossi Gandelsman Categories: cs.CV Arxiv: http://arxiv.org/abs/2410.24213v1 Title: Learning Video Representations without Natural Videos Abstract: In this paper, we show that useful video representations can be learned from synthetic videos and natural images, without incorporating natural videos in the training. We propose a progression of video datasets synthesized by simple generative processes, that model a growing set of

🤗 Daily Paper Upvotes: 10 Authors: Renze Lou, Hanzi Xu, Sijia Wang, Jiangshu Du, Ryo Kamoi, Xiaoxin Lu, Jian Xie, Yuxuan Sun, Yusen Zhang, Jihyun Janice Ahn, Hongchao Fang, Zhuoyang Zou, Wenchao Ma, Xi Li, Kai Zhang, Congying Xia, Lifu Huang, Wenpeng Yin Categories: cs.CL Arxiv: http://arxiv.org/abs/2410.22394v1 Title: AAAR-1.0: Assessing AI's Potential to Assist Research Abstract: Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs),

🤗 Daily Paper Upvotes: 7 Authors: Yang Zhou, Tan Li Hui Faith, Yanyu Xu, Sicong Leng, Xinxing Xu, Yong Liu, Rick Siow Mong Goh Categories: cs.CV Arxiv: http://arxiv.org/abs/2410.21969v1 Title: BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays Abstract: Medical Vision-Language Pretraining (MedVLP) shows promise in learning generalizable and transferable visual representations from paired and unpaired medical images and reports. MedVLP can provide