2 citations · 2 across the 9 of their papers we have counts for
10 papers
Multi-Image Visual Token Pruning in Large Visual Language Models
Rongyang Zhang, Chengqiang Lu, Cong Li +9
With the growing demand for processing multiple image sequences in real-world applications, various visual token pruning methods have emerged to mitigate the computational and cont…
Aligning Large Language Models with Searcher Preferences
Wei Wu, Peilun Zhou, Liyi Chen +6
The paradigm shift from item-centric ranking to answer-centric synthesis is redefining the role of search engines. While recent industrial progress has applied generative technique…
Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models
Wei Wu, Liyi Chen, Congxi Xiao +7
Large reasoning models enhanced by reinforcement learning with verifiable rewards have achieved significant performance gains by extending their chain-of-thought. However, this par…
RAG-IGBench: Innovative Evaluation for RAG-based Interleaved Generation in Open-domain Question Answering
Rongyang Zhang, Yuqing Huang, Chengqiang Lu +8
In real-world scenarios, providing user queries with visually enhanced responses can considerably benefit understanding and memory, underscoring the great value of interleaved imag…
RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios
Fei Zhao, Chengqiang Lu, Yufan Shen +9
While various multimodal multi-image evaluation datasets have been emerged, but these datasets are primarily based on English, and there has yet to be a Chinese multi-image dataset…
SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment
Yuqing Huang, Rongyang Zhang, Qimeng Wang +9
Recent advancements in large language models (LLMs) have revolutionized natural language processing through their remarkable capabilities in understanding and executing diverse tas…
Wide-Horizon Thinking and Simulation-Based Evaluation for Real-World LLM Planning with Multifaceted Constraints
Dongjie Yang, Chengqiang Lu, Qimeng Wang +4
Unlike reasoning, which often entails a deep sequence of deductive steps, complex real-world planning is characterized by the need to synthesize a broad spectrum of parallel and po…
MoDification: Mixture of Depths Made Easy
Chen Zhang, Meizhi Zhong, Qimeng Wang +8
Long-context efficiency has recently become a trending topic in serving large language models (LLMs). And mixture of depths (MoD) is proposed as a perfect fit to bring down both la…
Benchmarking Large Language Models for Conversational Question Answering in Multi-instructional Documents
Shiwei Wu, Chen Zhang, Yan Gao +4
Instructional documents are rich sources of knowledge for completing various tasks, yet their unique challenges in conversational question answering (CQA) have not been thoroughly…
VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation
Shiwei Wu, Joya Chen, Kevin Qinghong Lin +7
A well-known dilemma in large vision-language models (e.g., GPT-4, LLaVA) is that while increasing the number of vision tokens generally enhances visual understanding, it also sign…