6 citations · 15 across the 17 of their papers we have counts for
17 papers
Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs
Zhenyu Liu, Xuanyu Zhang, Yunxin Li +10
Developing seamless, high-performance, native intelligent full-duplex Spoken Language Models (SLMs) remains a critical challenge and long-standing goal for the speech and NLP commu…
CodeShrink: Adaptive Visual Compression for Efficient Multimodal Code Understanding
Wenxin Tang, Jingyu Xiao, Zhenyu Liu +6
Rendering source code as images offers a promising way to reduce the input costs of Multimodal Large Language Models (MLLMs). Adjusting image resolution can trade visual token cost…
A Theoretical Interpretation of In-Context Learning via Probabilistic Modeling
Zhenyu Liu, Huaze Tang, Shao-Lun Huang
In-context learning (ICL) is an emerging paradigm that employs the semantic information inherent in large language models (LLMs) for generating answers to user queries. While the r…
Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced MoE, Training and Data
Yunxin Li, Xinyu Chen, Shenyuan Jiang +9
We present Uni-MoE 2.0 from the Lychee family. As a fully open-source omnimodal large model (OLM), it substantially advances Lychee's Uni-MoE series in language-centric multimodal…
UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity MoE
Zhenyu Liu, Yunxin Li, Xuanyu Zhang +13
Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation. However, the auditory domain remains a significant challenge, with mus…
KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model
Xinping Zhao, Xinshuo Hu, Zifei Shan +14
Recent advancements in Large Language Models (LLMs)-based text embedding models primarily focus on data scaling or synthesis, yet limited exploration of training techniques and dat…
VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied Iterative Policy Optimization
Yunxin Li, Xinyu Chen, Zitao Li +5
Applying Reinforcement Learning (RL) to Video Large Language Models (Video-LLMs) shows significant promise for complex video reasoning. However, popular Reinforcement Fine-Tuning (…
Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models
Yunxin Li, Zhenyu Liu, Zitao Li +19
Reasoning lies at the heart of intelligence, shaping the ability to make decisions, draw conclusions, and generalize across domains. In artificial intelligence, as systems increasi…
Take Off the Training Wheels Progressive In-Context Learning for Effective Alignment
Zhenyu Liu, Dongfang Li, Xinshuo Hu +4
Recent studies have explored the working mechanisms of In-Context Learning (ICL). However, they mainly focus on classification and simple generation tasks, limiting their broader a…
Picking the Cream of the Crop: Visual-Centric Data Selection with Collaborative Agents
Zhenyu Liu, Yunxin Li, Baotian Hu +3
To improve Multimodal Large Language Models' (MLLMs) ability to process images and complex instructions, researchers predominantly curate large-scale visual instruction tuning data…
KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model
Xinshuo Hu, Zifei Shan, Xinping Zhao +10
As retrieval-augmented generation prevails in large language models, embedding models are becoming increasingly crucial. Despite the growing number of general embedding models, pri…
Medico: Towards Hallucination Detection and Correction with Multi-source Evidence Fusion
Xinping Zhao, Jindi Yu, Zhenyu Liu +5
As we all know, hallucinations prevail in Large Language Models (LLMs), where the generated content is coherent but factually incorrect, which inflicts a heavy blow on the widespre…
FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG
Xinping Zhao, Yan Zhong, Zetian Sun +5
Retrieval-Augmented Generation (RAG) prevails in Large Language Models. It mainly consists of retrieval and generation. The retrieval modules (a.k.a. retrievers) aim to find useful…
In-Context Learning State Vector with Inner and Momentum Optimization
Dongfang Li, Zhenyu Liu, Xinshuo Hu +3
Large Language Models (LLMs) have exhibited an impressive ability to perform In-Context Learning (ICL) from only a few examples. Recent works have indicated that the functions lear…
Improving Attributed Text Generation of Large Language Models via Preference Learning
Dongfang Li, Zetian Sun, Baotian Hu +4
Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content. Recent works aim to reduce misinformati…
A Survey of Large Language Models Attribution
Dongfang Li, Zetian Sun, Xinshuo Hu +5
Open-domain generative systems have gained significant attention in the field of conversational AI (e.g., generative search engines). This paper presents a comprehensive review of…
Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation
Xinshuo Hu, Dongfang Li, Baotian Hu +3
Large language models (LLMs) have been widely used in various applications but are known to suffer from issues related to untruthfulness and toxicity. While parameter-efficient mod…