18 citations · 56 across the 20 of their papers we have counts for
21 papers
CondenseGraph: Communication-Efficient Distributed GNN Training via On-the-Fly Graph Condensation
Zizhao Zhang, Yihan Xue, Haotian Zhu +3
Distributed Graph Neural Network (GNN) training suffers from substantial communication overhead due to the inherent neighborhood dependency in graph-structured data. This neighbor…
COMPASS: Enhancing Agent Long-Horizon Reasoning with Evolving Context
Guangya Wan, Mingyang Ling, Xiaoqi Ren +3
Long-horizon tasks that require sustained reasoning and multiple tool interactions remain challenging for LLM agents: small errors compound across steps, and even state-of-the-art…
SkillVerse : Assessing and Enhancing LLMs with Tree Evaluation
Yufei Tian, Jiao Sun, Nanyun Peng +1
As language models evolve to tackle complex, multifaceted tasks, their evaluation must adapt to capture this intricacy. A granular, skill-specific understanding of model capabiliti…
RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models
Bang An, Shiyue Zhang, Mark Dredze
Efforts to ensure the safety of large language models (LLMs) include safety fine-tuning, evaluation, and red teaming. However, despite the widespread use of the Retrieval-Augmented…
The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input
Alon Jacovi, Andrew Wang, Chris Alberti +23
We introduce FACTS Grounding, an online leaderboard and associated benchmark that evaluates language models' ability to generate text that is factually accurate with respect to giv…
RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation
Dongyu Ru, Lin Qiu, Xiangkun Hu +15
Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging external knowledge, a comprehensive evaluation of RAG systems is still challenging due to th…
OpenResearcher: Unleashing AI for Accelerated Scientific Research
Yuxiang Zheng, Shichao Sun, Lin Qiu +13
The rapid growth of scientific literature imposes significant challenges for researchers endeavoring to stay updated with the latest advancements in their fields and delve into new…
CodecLM: Aligning Language Models with Tailored Synthetic Data
Zifeng Wang, Chun-Liang Li, Vincent Perot +5
Instruction tuning has emerged as the key in aligning large language models (LLMs) with specific task instructions, thereby mitigating the discrepancy between the next-token predic…
RDBench: ML Benchmark for Relational Databases
Zizhao Zhang, Yi Yang, Lutong Zou +3
Benefiting from high-quality datasets and standardized evaluation metrics, machine learning (ML) has achieved sustained progress and widespread applications. However, while applyin…
ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction
Jingyi Yu, Zizhao Zhang, Shengfu Xia +1
We propose a novel end-to-end pipeline for online long-range vectorized high-definition (HD) map construction using on-board camera sensors. The vectorized representation of HD map…
Applications of Nature-Inspired Metaheuristic Algorithms for Tackling Optimization Problems Across Disciplines
Elvis Han Cui, Zizhao Zhang, Culsome Junwen Chen +1
Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging op…
Steering Prototypes with Prompt-tuning for Rehearsal-free Continual Learning
Zhuowei Li, Long Zhao, Zizhao Zhang +4
In the context of continual learning, prototypes-as representative class embeddings-offer advantages in memory conservation and the mitigation of catastrophic forgetting. However,…
StraIT: Non-autoregressive Generation with Stratified Image Transformer
Shengju Qian, Huiwen Chang, Yuanzhen Li +3
We propose Stratified Image Transformer(StraIT), a pure non-autoregressive(NAR) generative model that demonstrates superiority in high-quality image synthesis over existing autoreg…
QueryForm: A Simple Zero-shot Form Entity Query Framework
Zifeng Wang, Zizhao Zhang, Jacob Devlin +6
Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities. We presen…
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Xin Yu, Qi Yang, Yinchi Zhou +12
Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical…
DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning
Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi +8
Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store pa…
Learning Instance-Specific Adaptation for Cross-Domain Segmentation
Yuliang Zou, Zizhao Zhang, Chun-Liang Li +3
We propose a test-time adaptation method for cross-domain image segmentation. Our method is simple: Given a new unseen instance at test time, we adapt a pre-trained model by conduc…
Exploit Customer Life-time Value with Memoryless Experiments
Zizhao Zhang, Yifei Zhao, Guangda Huzhang
As a measure of the long-term contribution produced by customers in a service or product relationship, life-time value, or LTV, can more comprehensively find the optimal strategy f…
Learning Fast Sample Re-weighting Without Reward Data
Zizhao Zhang, Tomas Pfister
Training sample re-weighting is an effective approach for tackling data biases such as imbalanced and corrupted labels. Recent methods develop learning-based algorithms to learn sa…
Improved Transformer for High-Resolution GANs
Long Zhao, Zizhao Zhang, Ting Chen +2
Attention-based models, exemplified by the Transformer, can effectively model long range dependency, but suffer from the quadratic complexity of self-attention operation, making th…
Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding
Zizhao Zhang, Han Zhang, Long Zhao +3
Hierarchical structures are popular in recent vision transformers, however, they require sophisticated designs and massive datasets to work well. In this paper, we explore the idea…