8 papers
CodecCap: High-Fidelity Codec-Inspired Residual Modeling for Dense Video Captioning
Zihan Lin, Songhe Deng, Shuwei He +6
Existing video captioning methods struggle to balance visual fidelity and redundancy: holistic captions are compact but lose fine-grained evidence, whereas segment-wise captions im…
RotMoLE: Enhancing Mixture of Low-Rank Experts through Rotational Gating Mechanism
Mengyang Sun, Maochuan Dou, Tao Feng +5
While Large Language Models (LLMs) are commonly fine-tuned to handle domain-specific tasks before being applied to vertical applications, adapting them to complex scenarios with di…
R-Diverse: Mitigating Diversity Illusion in Self-Play LLM Training
Gengsheng Li, Jinghan He, Shijie Wang +7
Self-play bootstraps LLM reasoning through an iterative Challenger-Solver loop: the Challenger is trained to generate questions that target the Solver's capabilities, and the Solve…
Eureka-Audio: Triggering Audio Intelligence in Compact Language Models
Dan Zhang, Yishu Lei, Jing Hu +10
We present Eureka-Audio, a compact yet high-performance audio language model that achieves competitive performance against models that are 4 to 18 times larger across a broad range…
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
CORD: Bridging the Audio-Text Reasoning Gap via Weighted On-policy Cross-modal Distillation
Jing Hu, Danxiang Zhu, Xianlong Luo +9
Large Audio Language Models (LALMs) have garnered significant research interest. Despite being built upon text-based large language models (LLMs), LALMs frequently exhibit a degrad…