1 citations · 2 across the 9 of their papers we have counts for
9 papers
M2A: Multimodal Memory Agent with Dual-Layer Hybrid Memory for Long-Term Personalized Interactions
Junyu Feng, Binxiao Xu, Jiayi Chen +8
This work addresses the challenge of personalized question answering in long-term human-machine interactions: when conversational history spans weeks or months and exceeds the cont…
R2Q: Towards Robust 2-Bit Large Language Models via Residual Refinement Quantization
Jiayi Chen, Jieqi Shi, Jing Huo +1
The rapid progress of Large Language Models (LLMs) has brought substantial computational and memory demands, spurring the adoption of low-bit quantization. While 8-bit and 4-bit fo…
A Stylometric Application of Large Language Models
Harrison F. Stropkay, Jiayi Chen, Mohammad J. Latifi +2
We show that large language models (LLMs) can be used to distinguish the writings of different authors. Specifically, an individual GPT-2 model, trained from scratch on the works o…
A purely Quantum Generative Modeling through Unitary Scrambling and Collapse
Yihua Li, Jiayi Chen, Tamanna S. Kumavat +1
Quantum computing offers fundamentally more expressive mechanisms for generative modeling, yet current approaches remain constrained by classical neural components that bottleneck…
GDI-Bench: A Benchmark for General Document Intelligence with Vision and Reasoning Decoupling
Siqi Li, Yufan Shen, Xiangnan Chen +13
The rapid advancement of multimodal large language models (MLLMs) has profoundly impacted the document domain, creating a wide array of application scenarios. This progress highlig…
PIS: Linking Importance Sampling and Attention Mechanisms for Efficient Prompt Compression
Lizhe Chen, Binjia Zhou, Yuyao Ge +2
Large language models (LLMs) have achieved remarkable progress, demonstrating unprecedented capabilities across various natural language processing tasks. However, the high costs a…
PersLLM: A Personified Training Approach for Large Language Models
Zheni Zeng, Jiayi Chen, Huimin Chen +5
Large language models (LLMs) exhibit human-like intelligence, enabling them to simulate human behavior and support various applications that require both humanized communication an…
Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformation
Jiayi Chen, Rong Quan, Jie Qin
Cross-Domain Few-shot Semantic Segmentation (CD-FSS) aims to train generalized models that can segment classes from different domains with a few labeled images. Previous works have…
MCRAGE: Synthetic Healthcare Data for Fairness
Keira Behal, Jiayi Chen, Caleb Fikes +1
In the field of healthcare, electronic health records (EHR) serve as crucial training data for developing machine learning models for diagnosis, treatment, and the management of he…