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Found 7 papers, 2 papers with code
MMEKG: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities
Events are fundamental building blocks of real-world happenings.
Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective
Within our framework, we devise a causal graph to elucidate the predictions of MLLMs on VQA problems, and assess the causal effect of biases through an in-depth causal analysis.
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents.
Learning To Teach Large Language Models Logical Reasoning
Large language models (LLMs) have gained enormous attention from both academia and industry, due to their exceptional ability in language generation and extremely powerful generalization.
ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification
In this paper, we propose a novel Event Relational Graph TransfOrmer (ERGO) framework for DECI, which improves existing state-of-the-art (SOTA) methods upon two aspects.
Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction
We have conducted extensive experiments on three benchmarks, including both sentence- and document-level EAE.
r-GAT: Relational Graph Attention Network for Multi-Relational Graphs
To tackle this issue, we propose r-GAT, a relational graph attention network to learn multi-channel entity representations.
