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Shuming Ma, Hongyu Wang, Lingxiao Ma +7 authors
A 1-bit LLM variant, BitNet b1.58, achieves comparable performance to full-precision models with reduced computational costs and introduces new scaling laws and hardware design opportunities.
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Linrui Tian, Qi Wang, Bang Zhang +1 authors
EMO, a direct audio-to-video synthesis framework, enhances realism and expressiveness in talking head video generation by capturing nuanced audio-facial relationships without intermediate models.
Zhihong Shao, Peiyi Wang, Qihao Zhu +6 authors
DeepSeekMath 7B improves mathematical reasoning through enhanced data pre-training and Group Relative Policy Optimization, achieving high scores on MATH benchmark without external tools.
Zechun Liu, Changsheng Zhao, Forrest Iandola +9 authors
MobileLLM leverages deep, thin architectures and advanced techniques to achieve high accuracy in sub-billion parameter models suitable for mobile devices.
Pei Zhou, Jay Pujara, Xiang Ren +7 authors
SELF-DISCOVER is a framework enabling LLMs to generate task-specific reasoning structures, enhancing performance on complex reasoning tasks with reduced computational cost.
Yiran Ding, Li Lyna Zhang, Chengruidong Zhang +5 authors
LongRoPE extends pre-trained LLMs' context window to 2048k tokens with minimal fine-tuning costs and maintains original performance.
Xuezhi Wang, Denny Zhou
LLMs can perform chain-of-thought reasoning through top-k decoding without manual prompt engineering, outperforming greedy decoding and showing higher confidence in answers.
Kai Wang, Zhaopan Xu, Yukun Zhou +4 authors
Diffusion models can generate high-performing neural network parameters using an autoencoder, producing new subsets of network parameters with comparable or improved performance.
Yixin Liu, Kai Zhang, Yuan Li +9 authors
A comprehensive review of Sora, a text-to-video generative AI model, including its underlying technologies, applications, challenges, and future directions in video generation and human-AI interaction.
Dirk Groeneveld, Iz Beltagy, Pete Walsh +40 authors
OLMo, an open-sourced language model, provides comprehensive access to training data, code, and architecture, facilitating research and innovation in language modeling.
Tianyu Zheng, Ge Zhang, Tianhao Shen +5 authors
OpenCodeInterpreter, an open-source system for generating, executing, and refining code, achieves high performance on benchmarks through execution and human feedback, reducing the gap with proprietary systems like GPT-4 Code Interpreter.
Gagan Bhatia, El Moatez Billah Nagoudi, Hasan Cavusoglu +1 authors
FinTral, a multimodal LLM enhanced through domain-specific pretraining, instruction fine-tuning, and RLAIF, outperforms ChatGPT-3.5 and GPT-4 in financial analysis tasks with exceptional zero-shot performance.
Yaroslav Aksenov, Nikita Balagansky, Sofia Maria Lo Cicero Vaina +3 authors
A modification to the Based model kernel enhances its in-context learning capabilities, outperforming existing subquadratic architectures in language modeling tasks on the Pile dataset.
Jake Bruce, Michael Dennis, Ashley Edwards +22 authors
Genie, a 11B parameter unsupervised generative model, creates action-controllable virtual worlds from unlabelled videos using spatiotemporal tokenization and autoregressive dynamics, enabling agent training from unseen video behaviors.
Anian Ruoss, Grégoire Delétang, Sourabh Medapati +5 authors
A large-scale transformer model trained on a vast dataset of chess games outperforms traditional chess engines and other state-of-the-art models without domain-specific tweaks.
Yoad Tewel, Omri Kaduri, Rinon Gal +4 authors
ConsiStory achieves state-of-the-art text-to-image subject consistency without fine-tuning by using shared internal activations, attention blocks, and feature injection.
Luca Soldaini, Rodney Kinney, Akshita Bhagia +33 authors
Dolma, a three trillion token English corpus, and its open-source data curation toolkit facilitate open research in language model pretraining by providing detailed insights into data curation practices.
Mateusz Łajszczak, Guillermo Cámbara, Yang Li +16 authors
BASE TTS, a large-scale text-to-speech model, demonstrates advanced naturalness and emergent abilities using autoregressive Transformers and novel speech tokenization techniques.
Junyou Li, Qin Zhang, Yangbin Yu +2 authors
A sampling-and-voting method enhances large language models' performance by increasing the number of agents, with effectiveness tied to task difficulty.
Ruibin Yuan, Hanfeng Lin, Yi Wang +32 authors
ChatMusician, an open-source LLM with intrinsic musical abilities, outperforms GPT-4 in music generation and understanding tasks using text-compatible music notation.
Shivalika Singh, Freddie Vargus, Daniel Dsouza +30 authors
The initiative builds a human-curated instruction-following dataset spanning 65 languages and creates the largest multilingual collection of instruction-following instances through templating and translating existing datasets across 114 languages, contributing datasets and platforms for participatory research.
Niklas Muennighoff, Hongjin Su, Liang Wang +5 authors
GrIT allows large language models to excel at both generative and embedding tasks through task instruction, leading to new state-of-the-art performance without sacrificing efficiency.
Haoran Li, Qingxiu Dong, Zhengyang Tang +17 authors
GLAN, a general method for instruction tuning of LLMs, uses a taxonomy of human knowledge to generate synthetic instruction data, achieving strong performance across diverse tasks without task-specific training data.
Wei Huang, Yangdong Liu, Haotong Qin +5 authors
BiLLM, a 1-bit post-training quantization scheme for pretrained LLMs, achieves high-accuracy inference with reduced computational and memory requirements.
Chien-Yao Wang, I-Hau Yeh, Hong-Yuan Mark Liao
The paper addresses data loss in deep networks through programmable gradient information (PGI) and introduces the Generalized Efficient Layer Aggregation Network (GELAN) to improve parameter utilization and achieve competitive results in object detection.
Parth Sarthi, Salman Abdullah, Aditi Tuli +3 authors
The RAPTOR model enhances retrieval-augmented language models by recursively embedding, clustering, and summarizing text chunks, leading to better performance on question-answering tasks involving complex reasoning.
Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings +24 authors
Nemotron-4 15B, a large multilingual language model, excels in English, multilingual, and coding tasks, demonstrating superior performance in multilingual capabilities compared to larger and specialized models.
Zeyu Lu, Zidong Wang, Di Huang +4 authors
The Flexible Vision Transformer adapts to varied image resolutions and aspect ratios through dynamic tokenization and extrapolation techniques, outperforming traditional methods.
Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong +14 authors
Aya, a multilingual generative language model supporting over 50% lower-resourced languages, excels in both generative and discriminative tasks across 99 languages, and provides extensive evaluations and open-source resources.
Lucas Lehnert, Sainbayar Sukhbaatar, Paul Mcvay +2 authors
Searchformer, a Transformer model, outperforms traditional $A^*$ search by optimizing Sokoban puzzle solutions with fewer steps and demonstrates superior performance on maze navigation and more complex tasks.
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