2 citations · 2 across the 6 of their papers we have counts for
6 papers
Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning
Lu Dai, Ziyang Rao, Yili Wang +3
Fine-tuning LLMs to inject new knowledge faces a critical challenge: LLMs can quickly memorize new facts, yet fail to use them for downstream reasoning tasks. We formalize this fai…
LLM-Oriented Information Retrieval: A Denoising-First Perspective
Lu Dai, Liang Sun, Fanpu Cao +4
Modern information retrieval (IR) is no longer consumed primarily by humans but increasingly by large language models (LLMs) via retrieval-augmented generation (RAG) and agentic se…
SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction
Lu Dai, Yijie Xu, Jinhui Ye +2
Large Language Models (LLMs) have demonstrated improved generation performance by incorporating externally retrieved knowledge, a process known as retrieval-augmented generation (R…
Improve Dense Passage Retrieval with Entailment Tuning
Lu Dai, Hao Liu, Hui Xiong
Retrieval module can be plugged into many downstream NLP tasks to improve their performance, such as open-domain question answering and retrieval-augmented generation. The key to a…
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
Wenzhao Jiang, Hao Liu, Hui Xiong
Graph Neural Networks (GNNs) have emerged as powerful representation learning tools for capturing complex dependencies within diverse graph-structured data. Despite their success i…
Cloth2Body: Generating 3D Human Body Mesh from 2D Clothing
Lu Dai, Liqian Ma, Shenhan Qian +3
In this paper, we define and study a new Cloth2Body problem which has a goal of generating 3D human body meshes from a 2D clothing image. Unlike the existing human mesh recovery pr…