30 citations · 38 across the 9 of their papers we have counts for
11 papers
PIVOT: Perplexity-Informed KD-to-RL Transition Scheduling for Vertical-Domain Few-Shot Distillation
Heng Li, Yong Zhang, Ning Cheng +5
Vertical-domain few-shot classification remains challenging for small language models, as limited supervision makes it difficult to acquire domain-specific decision knowledge. On-P…
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…
WiseEdit: Benchmarking Cognition- and Creativity-Informed Image Editing
Kaihang Pan, Weile Chen, Haiyi Qiu +6
Recent image editing models boast next-level intelligent capabilities, facilitating cognition- and creativity-informed image editing. Yet, existing benchmarks provide too narrow a…
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…
Cluster Language Model for Improved E-Commerce Retrieval and Ranking: Leveraging Query Similarity and Fine-Tuning for Personalized Results
Duleep Rathgamage Don, Ying Xie, Le Yu +2
This paper proposes a novel method to improve the accuracy of product search in e-commerce by utilizing a cluster language model. The method aims to address the limitations of the…