4 citations · 15 across the 13 of their papers we have counts for
13 papers
Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence
NVIDIA, :, Amala Sanjay Deshmukh +204
We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 N…
CryptoX : Compositional Reasoning Evaluation of Large Language Models
Jiajun Shi, Chaoren Wei, Liqun Yang +7
The compositional reasoning capacity has long been regarded as critical to the generalization and intelligence emergence of large language models LLMs. However, despite numerous re…
The CHiME-8 DASR Challenge for Generalizable and Array Agnostic Distant Automatic Speech Recognition and Diarization
Samuele Cornell, Taejin Park, Steve Huang +6
This paper presents the CHiME-8 DASR challenge which carries on from the previous edition CHiME-7 DASR (C7DASR) and the past CHiME-6 challenge. It focuses on joint multi-channel di…
Instruction Data Generation and Unsupervised Adaptation for Speech Language Models
Vahid Noroozi, Zhehuai Chen, Somshubra Majumdar +3
In this paper, we propose three methods for generating synthetic samples to train and evaluate multimodal large language models capable of processing both text and speech inputs. A…
MuPT: A Generative Symbolic Music Pretrained Transformer
Xingwei Qu, Yuelin Bai, Yinghao Ma +25
In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music. While the prevalent use of MIDI in music modeling is well-established, our f…
The Fine Line: Navigating Large Language Model Pretraining with Down-streaming Capability Analysis
Chen Yang, Junzhuo Li, Xinyao Niu +11
Uncovering early-stage metrics that reflect final model performance is one core principle for large-scale pretraining. The existing scaling law demonstrates the power-law correlati…
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning
Xingwei Qu, Yiming Liang, Yucheng Wang +10
Large Language Models (LLMs) exhibit the ability to perform in-context learning (ICL), where they acquire new tasks directly from examples provided in demonstrations. This process…
CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation
Yujie Shao, Xinrong Yao, Xingwei Qu +5
Metaphor is a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication. This paper introduces a lar…
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic Spaces
Tianyu Zheng, Ge Zhang, Xingwei Qu +3
Drawing upon the intuition that aligning different modalities to the same semantic embedding space would allow models to understand states and actions more easily, we propose a new…
StructLM: Towards Building Generalist Models for Structured Knowledge Grounding
Alex Zhuang, Ge Zhang, Tianyu Zheng +7
Structured data sources, such as tables, graphs, and databases, are ubiquitous knowledge sources. Despite the demonstrated capabilities of large language models (LLMs) on plain tex…
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
Yizhi LI, Ge Zhang, Xingwei Qu +16
The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-fol…
ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation
Weiming Ren, Huan Yang, Ge Zhang +4
Image-to-video (I2V) generation aims to use the initial frame (alongside a text prompt) to create a video sequence. A grand challenge in I2V generation is to maintain visual consis…
Read to Play (R2-Play): Decision Transformer with Multimodal Game Instruction
Yonggang Jin, Ge Zhang, Hao Zhao +7
Developing a generalist agent is a longstanding objective in artificial intelligence. Previous efforts utilizing extensive offline datasets from various tasks demonstrate remarkabl…