11 citations · 11 across the 9 of their papers we have counts for
9 papers
Detecting Contextual Hallucinations in LLMs with Frequency-Aware Attention
Siya Qi, Yudong Chen, Runcong Zhao +6
Hallucination detection is critical for ensuring the reliability of large language models (LLMs) in context-based generation. Prior work has explored intrinsic signals available du…
Beyond Static Cropping: Layer-Adaptive Visual Localization and Decoding Enhancement
Zipeng Zhu, Zhanghao Hu, Qinglin Zhu +5
Large Vision-Language Models (LVLMs) have advanced rapidly by aligning visual patches with the text embedding space, but a fixed visual-token budget forces images to be resized to…
Beyond RAG for Agent Memory: Retrieval by Decoupling and Aggregation
Zhanghao Hu, Qinglin Zhu, Runcong Zhao +4
Standard Retrieval Augmented Generation (RAG) is poorly matched to agent memory. Unlike large heterogeneous corpora, agent memory forms a bounded and coherent interaction stream in…
Co-Investigator AI: The Rise of Agentic AI for Smarter, Trustworthy AML Compliance Narratives
Prathamesh Vasudeo Naik, Naresh Kumar Dintakurthi, Zhanghao Hu +2
Generating regulatorily compliant Suspicious Activity Report (SAR) remains a high-cost, low-scalability bottleneck in Anti-Money Laundering (AML) workflows. While large language mo…
Human Motion Video Generation: A Survey
Haiwei Xue, Xiangyang Luo, Zhanghao Hu +12
Human motion video generation has garnered significant research interest due to its broad applications, enabling innovations such as photorealistic singing heads or dynamic avatars…
Beyond Perplexity: Let the Reader Select Retrieval Summaries via Spectrum Projection Score
Zhanghao Hu, Qinglin Zhu, Siya Qi +3
Large Language Models (LLMs) have shown improved generation performance through retrieval-augmented generation (RAG) following the retriever-reader paradigm, which supplements mode…
Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering
Zhanghao Hu, Hanqi Yan, Qinglin Zhu +3
Large language models have recently pushed open domain question answering (ODQA) to new frontiers. However, prevailing retriever-reader pipelines often depend on multiple rounds of…
CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation
Zhenyi Shen, Hanqi Yan, Linhai Zhang +3
Chain-of-Thought (CoT) reasoning enhances Large Language Models (LLMs) by encouraging step-by-step reasoning in natural language. However, leveraging a latent continuous space for…
EEE-QA: Exploring Effective and Efficient Question-Answer Representations
Zhanghao Hu, Yijun Yang, Junjie Xu +2
Current approaches to question answering rely on pre-trained language models (PLMs) like RoBERTa. This work challenges the existing question-answer encoding convention and explores…