1 citations · 2 across the 7 of their papers we have counts for
7 papers
SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs
Jie Sun, Yu Liu, Lu Han +9
While transformer-based Large Language Models (LLMs) theoretically support massive context windows, they suffer from severe performance degradation when processing long numerical s…
Kwai Keye-VL 1.5 Technical Report
Biao Yang, Bin Wen, Boyang Ding +58
In recent years, the development of Large Language Models (LLMs) has significantly advanced, extending their capabilities to multimodal tasks through Multimodal Large Language Mode…
Kwai Keye-VL Technical Report
Kwai Keye Team, Biao Yang, Bin Wen +57
While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities on static images, they often fall short in comprehending dynamic, information-dense short-form vi…
R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement Learning
Yi-Fan Zhang, Xingyu Lu, Xiao Hu +13
Multimodal Reward Models (MRMs) play a crucial role in enhancing the performance of Multimodal Large Language Models (MLLMs). While recent advancements have primarily focused on im…
Aligning Multimodal LLM with Human Preference: A Survey
Tao Yu, Yi-Fan Zhang, Chaoyou Fu +14
Large language models (LLMs) can handle a wide variety of general tasks with simple prompts, without the need for task-specific training. Multimodal Large Language Models (MLLMs),…
Scaling Laws for Fact Memorization of Large Language Models
Xingyu Lu, Xiaonan Li, Qinyuan Cheng +3
Fact knowledge memorization is crucial for Large Language Models (LLM) to generate factual and reliable responses. However, the behaviors of LLM fact memorization remain under-expl…
Leveraging Contextual Information for Effective Entity Salience Detection
Rajarshi Bhowmik, Marco Ponza, Atharva Tendle +5
In text documents such as news articles, the content and key events usually revolve around a subset of all the entities mentioned in a document. These entities, often deemed as sal…