Summary of Lime: Less Is More For Mllm Evaluation, by King Zhu et al.
LIME: Less Is More for MLLM Evaluationby King Zhu, Qianbo Zang, Shian Jia, Siwei Wu,…
Summary of Accelerating Large Language Model Pretraining Via Lfr Pedagogy: Learn, Focus, and Review, by Neha Prakriya et al.
Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Reviewby Neha Prakriya, Jui-Nan…
Summary of Eyeclip: a Visual-language Foundation Model For Multi-modal Ophthalmic Image Analysis, by Danli Shi et al.
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysisby Danli Shi, Weiyi Zhang, Jiancheng…
Summary of Rirag: Regulatory Information Retrieval and Answer Generation, by Tuba Gokhan and Kexin Wang and Iryna Gurevych and Ted Briscoe
RIRAG: Regulatory Information Retrieval and Answer Generationby Tuba Gokhan, Kexin Wang, Iryna Gurevych, Ted BriscoeFirst…
Summary of Seek and Solve Reasoning For Table Question Answering, by Ruya Jiang and Chun Wang and Weihong Deng
Seek and Solve Reasoning for Table Question Answeringby Ruya Jiang, Chun Wang, Weihong DengFirst submitted…
Summary of Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models, by Xingyun Hong et al.
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Modelsby Xingyun Hong,…
Summary of Question-answering Dense Video Events, by Hangyu Qin et al.
Question-Answering Dense Video Eventsby Hangyu Qin, Junbin Xiao, Angela YaoFirst submitted to arxiv on: 6…
Summary of Columbus: Evaluating Cognitive Lateral Understanding Through Multiple-choice Rebuses, by Koen Kraaijveld et al.
COLUMBUS: Evaluating COgnitive Lateral Understanding through Multiple-choice reBUSesby Koen Kraaijveld, Yifan Jiang, Kaixin Ma, Filip…
Summary of Debate on Graph: a Flexible and Reliable Reasoning Framework For Large Language Models, by Jie Ma et al.
Debate on Graph: a Flexible and Reliable Reasoning Framework for Large Language Modelsby Jie Ma,…
Summary of R2gqa: Retriever-reader-generator Question Answering System to Support Students Understanding Legal Regulations in Higher Education, by Phuc-tinh Pham Do et al.
R2GQA: Retriever-Reader-Generator Question Answering System to Support Students Understanding Legal Regulations in Higher Educationby Phuc-Tinh…