Search Results for author:
Found 12 papers, 6 papers with code
MMEKG: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities
Events are fundamental building blocks of real-world happenings.
CRAT: A Multi-Agent Framework for Causality-Enhanced Reflective and Retrieval-Augmented Translation with Large Language Models
In this paper, we propose CRAT, a novel multi-agent translation framework that leverages RAG and causality-enhanced self-reflection to address these challenges.
Navigating the Nuances: A Fine-grained Evaluation of Vision-Language Navigation
The framework is structured around the context-free grammar (CFG) of the task.
MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations
Moreover, 33. 2% of the questions are cross-page questions requiring evidence across multiple pages.
CELLO: Causal Evaluation of Large Vision-Language Models
Previous work typically focuses on commonsense causality between events and/or actions, which is insufficient for applications like embodied agents and lacks the explicitly defined causal graphs required for formal causal reasoning.
Causal Evaluation of Language Models
Recent advances in language models have expanded the horizons of artificial intelligence across various domains, sparking inquiries into their potential for causal reasoning.
Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective
Within this framework, we conduct an in-depth causal analysis to assess the causal effect of these biases on MLLM predictions.
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.
Improving Large Language Models in Event Relation Logical Prediction
More in detail, we first investigate the deficiencies of LLMs in logical reasoning across different tasks.
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.
