6 citations · 25 across the 24 of their papers we have counts for
9 papers · 1 filter
Delving into the Reversal Curse: How Far Can Large Language Models Generalize?
Zhengkai Lin, Zhihang Fu, Kai Liu +6
While large language models (LLMs) showcase unprecedented capabilities, they also exhibit certain inherent limitations when facing seemingly trivial tasks. A prime example is the r…
Seeing Clearly by Layer Two: Enhancing Attention Heads to Alleviate Hallucination in LVLMs
Xiaofeng Zhang, Yihao Quan, Chaochen Gu +6
The hallucination problem in multimodal large language models (MLLMs) remains a common issue. Although image tokens occupy a majority of the input sequence of MLLMs, there is limit…
Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding
Deyi Ji, Lanyun Zhu, Siqi Gao +4
The ubiquity and value of tables as semi-structured data across various domains necessitate advanced methods for understanding their complexity and vast amounts of information. Des…
Instance-adaptive Zero-shot Chain-of-Thought Prompting
Xiaosong Yuan, Chen Shen, Shaotian Yan +6
Zero-shot Chain-of-Thought (CoT) prompting emerges as a simple and effective strategy for enhancing the performance of large language models (LLMs) in real-world reasoning tasks. N…
SciPIP: An LLM-based Scientific Paper Idea Proposer
Wenxiao Wang, Lihui Gu, Liye Zhang +7
The rapid advancement of large language models (LLMs) has opened new possibilities for automating the proposal of innovative scientific ideas. This process involves two key phases:…
URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering
Ge Teng, Ting Mao, Chen Shen +4
Incomplete multi-view clustering (IMVC) aims to cluster multi-view data that are only partially available. This poses two main challenges: effectively leveraging multi-view informa…
Discrete Latent Perspective Learning for Segmentation and Detection
Deyi Ji, Feng Zhao, Lanyun Zhu +3
In this paper, we address the challenge of Perspective-Invariant Learning in machine learning and computer vision, which involves enabling a network to understand images from varyi…
From Redundancy to Relevance: Information Flow in LVLMs Across Reasoning Tasks
Xiaofeng Zhang, Yihao Quan, Chen Shen +7
Large Vision Language Models (LVLMs) achieve great performance on visual-language reasoning tasks, however, the black-box nature of LVLMs hinders in-depth research on the reasoning…
IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding
Lanyun Zhu, Deyi Ji, Tianrun Chen +3
Despite achieving rapid developments and with widespread applications, Large Vision-Language Models (LVLMs) confront a serious challenge of being prone to generating hallucinations…