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Junsong Chen, Yue Wu, Simian Luo +5 authors
PIXART-{\delta} integrates Latent Consistency Model (LCM) and ControlNet into PIXART-{\alpha} for accelerated, high-quality text-to-image synthesis with efficient training and inference.
Moab Arar, Andrey Voynov, Amir Hertz +5 authors
Prompt-aligned personalization improves text alignment and enhances personalized image creation with complex prompts by using score distillation sampling.
Duzhen Zhang, Yahan Yu, Chenxing Li +4 authors
A survey examining the design, performance, and future directions of MultiModal Large Language Models (MM-LLMs) to enhance reasoning and MM task capabilities.
Sijie Cheng, Kechen Fang, Yangyang Yu +6 authors
VidEgoThink, a comprehensive benchmark for evaluating egocentric video understanding capabilities, highlights the need for advancements in Multi-modal Large Language Models for effective application in Embodied AI.
Zimu Lu, Aojun Zhou, Ke Wang +5 authors
A novel method for generating mathematical code with reasoning steps enhances mathematical reasoning abilities in large language models using a comprehensive dataset named MathCode-Pile.
Yifan Xu, Xiao Liu, Xueqiao Sun +7 authors
AndroidLab provides a systematic framework for training and evaluating Android agents, supporting both large language models and multimodal models, and improves their task success rates.
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.
Jianzong Wu, Chao Tang, Jingbo Wang +3 authors
DiffSensei, a diffusion-based framework with a multimodal large language model, enhances manga generation by precisely controlling multi-character appearances and interactions using masked cross-attention and text cues.
Xun Liang, Shichao Song, Zifan Zheng +6 authors
A theoretical framework called Internal Consistency and a Self-Feedback framework with Self-Evaluation and Self-Update modules are presented to address reasoning and hallucination in large language models by assessing coherence across model layers.
Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy +6 authors
Multiple RLHF algorithms, including Expert Iteration, PPO, and Return-Conditioned RL, similarly improve LLM reasoning with similar sample complexity, with Expert Iteration slightly outperforming others.
Jian Yang, Jiaxi Yang, Ke Jin +7 authors
A human-curated benchmark (CodeArena) and a large synthetic instruction corpus (SynCode-Instruct) are introduced to evaluate code LLMs based on human preference alignment, revealing performance differences between open-source and proprietary models.
Zhisheng Zhong, Chengyao Wang, Yuqi Liu +12 authors
As Multi-modal Large Language Models (MLLMs) evolve, expanding beyond single-domain capabilities is essential to meet the demands for more versatile and efficient AI. However, previous omni-models have insufficiently explored speech, neglecting its integration with multi-modality. We introduce Lyra, an efficient MLLM that enhances multimodal abilities, including advanced long-speech comprehension, sound understanding, cross-modality efficiency, and seamless speech interaction. To achieve efficiency and speech-centric capabilities, Lyra employs three strategies: (1) leveraging existing open-source large models and a proposed multi-modality LoRA to reduce training costs and data requirements; (2) using a latent multi-modality regularizer and extractor to strengthen the relationship between speech and other modalities, thereby enhancing model performance; and (3) constructing a high-quality, extensive dataset that includes 1.5M multi-modal (language, vision, audio) data samples and 12K long speech samples, enabling Lyra to handle complex long speech inputs and achieve more robust omni-cognition. Compared to other omni-methods, Lyra achieves state-of-the-art performance on various vision-language, vision-speech, and speech-language benchmarks, while also using fewer computational resources and less training data.
Enrico Fini, Mustafa Shukor, Xiujun Li +13 authors
AIMV2, a multimodal vision encoder paired with an autoregressive decoder, achieves superior performance in vision benchmarks and multimodal image understanding tasks compared to contrastive models.
Johannes Schmude, Sujit Roy, Will Trojak +26 authors
Prithvi WxC, a 2.3 billion parameter foundation model using an encoder-decoder architecture with transformer concepts, addresses weather forecasting, downscaling, and extreme events estimation with a mixed masked reconstruction and forecasting objective.
Rogerio Bonatti, Dan Zhao, Francesco Bonacci +8 authors
Windows Agent Arena provides a general, reproducible environment for evaluating multi-modal agent performance on Windows OS tasks, with scalability and parallelizability features.
Chenglei Si, Diyi Yang, Tatsunori Hashimoto
LLM-generated research ideas are perceived as more novel than those from human experts but are deemed slightly less feasible, based on blind evaluations by NLP researchers.
jiajie Zhang, Yushi Bai, Xin Lv +8 authors
A new pipeline and dataset enable long-context LLMs to generate responses with precise sentence-level citations, improving their trustworthiness and outperforming existing models.
Wei Chen, Zhiyuan Li, Zhen Guo +1 authors
An on-device Planner-Action framework, utilizing Phi-3 Mini for planning and Octopus for execution, employs model fine-tuning and multi-LoRA training to enhance efficiency and multi-domain handling on resource-constrained devices.
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.
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.
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.
Fu-Yun Wang, Zhaoyang Huang, Alexander William Bergman +9 authors
The Phased Consistency Model (PCM) addresses limitations in Latent Consistency Models (LCM) and outperforms them in high-resolution, text-conditioned image and few-step text-to-video generation.
Ge Zhang, Scott Qu, Jiaheng Liu +42 authors
MAP-Neo, a fully open-sourced bilingual LLM with 7B parameters, achieves performance comparable to existing state-of-the-art LLMs, enhancing transparency and fostering innovation in the open research community.
Guanting Dong, Xiaoshuai Song, Yutao Zhu +3 authors
VIF-RAG-QA and FollowRAG Benchmark enhance Instruction-Following alignment in RAG systems through automated instruction synthesis, verification, and evaluation.
Yushen Chen, Zhikang Niu, Ziyang Ma +5 authors
F5-TTS, a fully non-autoregressive text-to-speech system, improves E2 TTS by leveraging ConvNeXt and Sway Sampling for better performance and efficiency.
Weihao Zeng, Yuzhen Huang, Lulu Zhao +3 authors
B-STaR framework autonomously adjusts configurations to enhance exploration and exploitation, improving performance in self-improving models.
Hadas Orgad, Michael Toker, Zorik Gekhman +4 authors
LLMs internally encode detailed truthfulness information about their outputs, which can be used to detect and predict errors but shows variability across datasets and does not always align with the final output.
Zhuoqun Li, Xuanang Chen, Haiyang Yu +7 authors
StructRAG enhances LLMs by identifying optimal structure types, reconstructing documents into these structures, and inferring answers for knowledge-intensive reasoning tasks, achieving state-of-the-art performance.
Sijun Tan, Siyuan Zhuang, Kyle Montgomery +5 authors
JudgeBench is a benchmark for evaluating LLM-based judges using objective correctness for challenging tasks like knowledge, reasoning, math, and coding.
Junyu Luo, Xiao Luo, Xiusi Chen +3 authors
A semi-supervised fine-tuning framework named SemiEvol enhances LLM adaptation using both labeled and unlabeled data, showing improved performance through bi-level knowledge propagation and collaborative learning.
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