7 papers
Multi-Image Visual Token Pruning in Large Visual Language Models
Rongyang Zhang, Chengqiang Lu, Cong Li +9
With the growing demand for processing multiple image sequences in real-world applications, various visual token pruning methods have emerged to mitigate the computational and cont…
DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?
Jiahui tang, Kuicai Dong, Dexun Li +7
Faithful chart generation in real-world data-science workflows requires grounding visualizations in scattered evidence, computing chart-ready quantities, and rendering them accurat…
MACReD: A Multi-Agent Collaborative Reasoning Framework for Reaction Diagram Parsing
Chuang Tang, Chenhao Lin, Yin Xu +5
Parsing chemical reaction diagrams from scientific literature is challenging due to heterogeneous layouts, intertwined visual elements, and the difficulty of integrating recognitio…
SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment
Yuqing Huang, Rongyang Zhang, Qimeng Wang +9
Recent advancements in large language models (LLMs) have revolutionized natural language processing through their remarkable capabilities in understanding and executing diverse tas…
Locating and Mitigating Gender Bias in Large Language Models
Yuchen Cai, Ding Cao, Rongxi Guo +3
Large language models(LLM) are pre-trained on extensive corpora to learn facts and human cognition which contain human preferences. However, this process can inadvertently lead to…
Editing Knowledge Representation of Language Model via Rephrased Prefix Prompts
Yuchen Cai, Ding Cao, Rongxi Guo +3
Neural language models (LMs) have been extensively trained on vast corpora to store factual knowledge about various aspects of the world described in texts. Current technologies ty…
Towards Generalist Prompting for Large Language Models by Mental Models
Haoxiang Guan, Jiyan He, Shuxin Zheng +3
Large language models (LLMs) have demonstrated impressive performance on many tasks. However, to achieve optimal performance, specially designed prompting methods are still needed.…