3 citations · 10 across the 20 of their papers we have counts for
7 papers · 1 filter
From Competition to Synergy: Unlocking Reinforcement Learning for Subject-Driven Image Generation
Ziwei Huang, Ying Shu, Hao Fang +5
Subject-driven image generation models face a fundamental trade-off between identity preservation (fidelity) and prompt adherence (editability). While online reinforcement learning…
Causality Matters: How Temporal Information Emerges in Video Language Models
Yumeng Shi, Quanyu Long, Yin Wu +1
Video language models (VideoLMs) have made significant progress in multimodal understanding. However, temporal understanding, which involves identifying event order, duration, and…
Coordinating Search-Informed Reasoning and Reasoning-Guided Search in Claim Verification
Qisheng Hu, Quanyu Long, Wenya Wang
Multi-hop claim verification is inherently challenging, requiring multi-step reasoning to construct verification chains while iteratively searching for information to uncover hidde…
Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts
Quanyu Long, Jianda Chen, Zhengyuan Liu +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks. While retrieval-augme…
Static or Dynamic: Towards Query-Adaptive Token Selection for Video Question Answering
Yumeng Shi, Quanyu Long, Wenya Wang
Video question answering benefits from the rich information in videos, enabling various applications. However, the large volume of tokens generated from long videos presents challe…
BOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking
Qisheng Hu, Quanyu Long, Wenya Wang
Large language model pipelines have improved automated fact-checking for complex claims, yet many approaches rely on few-shot in-context learning with demonstrations that require s…
Visual-RAG: Benchmarking Text-to-Image Retrieval Augmented Generation for Visual Knowledge Intensive Queries
Yin Wu, Quanyu Long, Jing Li +2
Retrieval-augmented generation (RAG) is a paradigm that augments large language models (LLMs) with external knowledge to tackle knowledge-intensive question answering. While severa…