Summary of Easy Problems That Llms Get Wrong, by Sean Williams et al.
Easy Problems That LLMs Get Wrongby Sean Williams, James HuckleFirst submitted to arxiv on: 30…
Summary of Evaluating Zero-shot Gpt-4v Performance on 3d Visual Question Answering Benchmarks, by Simranjit Singh et al.
Evaluating Zero-Shot GPT-4V Performance on 3D Visual Question Answering Benchmarksby Simranjit Singh, Georgios Pavlakos, Dimitrios…
Summary of Don’t Forget to Connect! Improving Rag with Graph-based Reranking, by Jialin Dong et al.
Don’t Forget to Connect! Improving RAG with Graph-based Rerankingby Jialin Dong, Bahare Fatemi, Bryan Perozzi,…
Summary of Towards Efficient Llm Grounding For Embodied Multi-agent Collaboration, by Yang Zhang et al.
Towards Efficient LLM Grounding for Embodied Multi-Agent Collaborationby Yang Zhang, Shixin Yang, Chenjia Bai, Fei…
Summary of Preparing For Black Swans: the Antifragility Imperative For Machine Learning, by Ming Jin
Preparing for Black Swans: The Antifragility Imperative for Machine Learningby Ming JinFirst submitted to arxiv…
Summary of Compositional Text-to-image Generation with Dense Blob Representations, by Weili Nie et al.
Compositional Text-to-Image Generation with Dense Blob Representationsby Weili Nie, Sifei Liu, Morteza Mardani, Chao Liu,…
Summary of Improving Intervention Efficacy Via Concept Realignment in Concept Bottleneck Models, by Nishad Singhi et al.
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Modelsby Nishad Singhi, Jae Myung Kim,…
Summary of A Self-explaining Neural Architecture For Generalizable Concept Learning, by Sanchit Sinha et al.
A Self-explaining Neural Architecture for Generalizable Concept Learningby Sanchit Sinha, Guangzhi Xiong, Aidong ZhangFirst submitted…
Summary of Naturally Supervised 3d Visual Grounding with Language-regularized Concept Learners, by Chun Feng et al.
Naturally Supervised 3D Visual Grounding with Language-Regularized Concept Learnersby Chun Feng, Joy Hsu, Weiyu Liu,…
Summary of Q-groundcam: Quantifying Grounding in Vision Language Models Via Gradcam, by Navid Rajabi et al.
Q-GroundCAM: Quantifying Grounding in Vision Language Models via GradCAMby Navid Rajabi, Jana KoseckaFirst submitted to…