30 citations · 38 across the 9 of their papers we have counts for
8 papers · 1 filter
Astra: Activation-Space Tail-Eigenvector Low-Rank Adaptation of Large Language Models
Kainan Liu, Yong Zhang, Ning Cheng +4
Parameter-Efficient Fine-Tuning (PEFT) methods, especially LoRA, are widely used for adapting pre-trained models to downstream tasks due to their computational and storage efficien…
System-1.5 Reasoning: Traversal in Language and Latent Spaces with Dynamic Shortcuts
Xiaoqiang Wang, Suyuchen Wang, Yun Zhu +1
Chain-of-thought (CoT) reasoning enables large language models (LLMs) to move beyond fast System-1 responses and engage in deliberative System-2 reasoning. However, this comes at t…
RMem: Bridging Memory Retention and Retrieval via Reversible Compression
Xiaoqiang Wang, Suyuchen Wang, Yun Zhu +1
Memory plays a key role in enhancing LLMs' performance when deployed to real-world applications. Existing solutions face trade-offs: explicit memory designs based on external stora…
FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrieval
Jinlin Wang, Suyuchen Wang, Ziwen Xia +4
Large Language Models (LLMs) are proficient at retrieving single facts from extended contexts, yet they struggle with tasks requiring the simultaneous retrieval of multiple facts,…
LLaSA: Large Language and E-Commerce Shopping Assistant
Shuo Zhang, Boci Peng, Xinping Zhao +4
The e-commerce platform has evolved rapidly due to its widespread popularity and convenience. Developing an e-commerce shopping assistant for customers is crucial to aiding them in…
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection
Yun Zhu, Jia-Chen Gu, Caitlin Sikora +8
Large language models (LLMs) augmented with retrieval exhibit robust performance and extensive versatility by incorporating external contexts. However, the input length grows linea…
Inducing Generalization across Languages and Tasks using Featurized Low-Rank Mixtures
Chu-Cheng Lin, Xinyi Wang, Jonathan H. Clark +4
Adapting pretrained large language models (LLMs) to various downstream tasks in tens or hundreds of human languages is computationally expensive. Parameter-efficient fine-tuning (P…
Corrective Retrieval Augmented Generation
Shi-Qi Yan, Jia-Chen Gu, Yun Zhu +1
Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of generated texts cannot be secured solely by the parametric knowledge they encapsulate. Although…