8 papers
Understand Then Memory: A Cognitive Gist-Driven RAG Framework with Global Semantic Diffusion
Pengcheng Zhou, Haochen Li, Zhiqiang Nie +4
Retrieval-Augmented Generation (RAG) effectively mitigates hallucinations in LLMs by incorporating external knowledge. However, the inherent discrete representation of text in exis…
AECBench: A Hierarchical Benchmark for Knowledge Evaluation of Large Language Models in the AEC Field
Chen Liang, Zhaoqi Huang, Haofen Wang +8
Large language models (LLMs), as a novel information technology, are seeing increasing adoption in the Architecture, Engineering, and Construction (AEC) field. They have shown thei…
Monkey Jump : MoE-Style PEFT for Efficient Multi-Task Learning
Nusrat Jahan Prottasha, Md Kowsher, Chun-Nam Yu +2
Mixture-of-experts variants of parameter-efficient fine-tuning enable per-token specialization, but they introduce additional trainable routers and expert parameters, increasing me…
Multivariate Diffusion Transformer with Decoupled Attention for High-Fidelity Mask-Text Collaborative Facial Generation
Yushe Cao, Dianxi Shi, Xing Fu +5
While significant progress has been achieved in multimodal facial generation using semantic masks and textual descriptions, conventional feature fusion approaches often fail to ena…
LLMartini: Seamless and Interactive Leveraging of Multiple LLMs through Comparison and Composition
Yingtian Shi, Jinda Yang, Yuhan Wang +4
The growing diversity of large language models (LLMs) means users often need to compare and combine outputs from different models to obtain higher-quality or more comprehensive res…
RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates
Md Kowsher, Tara Esmaeilbeig, Chun-Nam Yu +3
We propose RoCoFT, a parameter-efficient fine-tuning method for large-scale language models (LMs) based on updating only a few rows and columns of the weight matrices in transforme…