14 citations · 57 across the 29 of their papers we have counts for
15 papers · 1 filter
Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization
Gyuseok Lee, Wonbin Kweon, Zhenrui Yue +3
Reward factorization personalizes large language models (LLMs) by decomposing rewards into shared basis functions and user-specific weights. Yet, existing methods estimate user wei…
FASA: Frequency-aware Sparse Attention
Yifei Wang, Yueqi Wang, Zhenrui Yue +6
The deployment of Large Language Models (LLMs) faces a critical bottleneck when handling lengthy inputs: the prohibitive memory footprint of the Key Value (KV) cache. To address th…
Hybrid Latent Reasoning via Reinforcement Learning
Zhenrui Yue, Bowen Jin, Huimin Zeng +6
Recent advances in large language models (LLMs) have introduced latent reasoning as a promising alternative to autoregressive reasoning. By performing internal computation with hid…
Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs?
Hansi Zeng, Kai Hui, Honglei Zhuang +4
While metrics available during pre-training, such as perplexity, correlate well with model performance at scaling-laws studies, their predictive capacities at a fixed model size re…
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Bowen Jin, Hansi Zeng, Zhenrui Yue +5
Efficiently acquiring external knowledge and up-to-date information is essential for effective reasoning and text generation in large language models (LLMs). Prompting advanced LLM…
Inference Scaling for Long-Context Retrieval Augmented Generation
Zhenrui Yue, Honglei Zhuang, Aijun Bai +7
The scaling of inference computation has unlocked the potential of long-context large language models (LLMs) across diverse settings. For knowledge-intensive tasks, the increased c…
Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments
Zhenrui Yue, Huimin Zeng, Lanyu Shang +3
The rapid propagation of misinformation poses substantial risks to public interest. To combat misinformation, large language models (LLMs) are adapted to automatically verify claim…
Open-Vocabulary Federated Learning with Multimodal Prototyping
Huimin Zeng, Zhenrui Yue, Dong Wang
Existing federated learning (FL) studies usually assume the training label space and test label space are identical. However, in real-world applications, this assumption is too ide…
Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation
Zhenrui Yue, Huimin Zeng, Yimeng Lu +3
The proliferation of online misinformation has posed significant threats to public interest. While numerous online users actively participate in the combat against misinformation,…
Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning
Zhenrui Yue, Huimin Zeng, Mengfei Lan +2
With emerging online topics as a source for numerous new events, detecting unseen / rare event types presents an elusive challenge for existing event detection methods, where only…
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning
Zhenrui Yue, Huimin Zeng, Yang Zhang +2
With emerging topics (e.g., COVID-19) on social media as a source for the spreading misinformation, overcoming the distributional shifts between the original training domain (i.e.,…
QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation
Zhenrui Yue, Huimin Zeng, Bernhard Kratzwald +2
Question answering (QA) has recently shown impressive results for answering questions from customized domains. Yet, a common challenge is to adapt QA models to an unseen target dom…
Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup
Huimin Zeng, Zhenrui Yue, Ziyi Kou +3
In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the mo…
Domain Adaptation for Question Answering via Question Classification
Zhenrui Yue, Huimin Zeng, Ziyi Kou +2
Question answering (QA) has demonstrated impressive progress in answering questions from customized domains. Nevertheless, domain adaptation remains one of the most elusive challen…
Contrastive Domain Adaptation for Question Answering using Limited Text Corpora
Zhenrui Yue, Bernhard Kratzwald, Stefan Feuerriegel
Question generation has recently shown impressive results in customizing question answering (QA) systems to new domains. These approaches circumvent the need for manually annotated…