1 citations · 1 across the 8 of their papers we have counts for
8 papers
FilmBench: A Film-Grade Benchmark for Cinematic Video Generation
Shengyi Wang, Niantong Li, Guangzheng Hu +27
Progress in video generation keeps narrowing the visual gap between AI-generated and professionally produced footage, yet most benchmarks still draw prompts from web sources or LLM…
Merging Beyond: Streaming LLM Updates via Activation-Guided Rotations
Yuxuan Yao, Haonan Sheng, Qingsong Lv +11
The escalating scale of Large Language Models (LLMs) necessitates efficient adaptation techniques. Model merging has gained prominence for its efficiency and controllability. Howev…
LoViC: Efficient Long Video Generation with Context Compression
Jiaxiu Jiang, Wenbo Li, Jingjing Ren +5
Despite recent advances in diffusion transformers (DiTs) for text-to-video generation, scaling to long-duration content remains challenging due to the quadratic complexity of self-…
Molly: Making Large Language Model Agents Solve Python Problem More Logically
Rui Xiao, Jiong Wang, Lu Han +2
Applying large language models (LLMs) as teaching assists has attracted much attention as an integral part of intelligent education, particularly in computing courses. To reduce th…
CoCA: Regaining Safety-awareness of Multimodal Large Language Models with Constitutional Calibration
Jiahui Gao, Renjie Pi, Tianyang Han +5
The deployment of multimodal large language models (MLLMs) has demonstrated remarkable success in engaging in conversations involving visual inputs, thanks to the superior power of…
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling
Yuxuan Yao, Han Wu, Mingyang Liu +5
Large language models (LLMs) exhibit varying strengths and weaknesses across different tasks, prompting recent studies to explore the benefits of ensembling models to leverage thei…
Learning From Correctness Without Prompting Makes LLM Efficient Reasoner
Yuxuan Yao, Han Wu, Zhijiang Guo +6
Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and tox…
Towards Versatile and Efficient Visual Knowledge Integration into Pre-trained Language Models with Cross-Modal Adapters
Xinyun Zhang, Haochen Tan, Han Wu +1
Humans learn language via multi-modal knowledge. However, due to the text-only pre-training scheme, most existing pre-trained language models (PLMs) are hindered from the multi-mod…