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Found 7 papers, 6 papers with code
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge Distillation
Automatic detection of multimodal misinformation has gained a widespread attention recently.
A Survey on Knowledge Distillation of Large Language Models
In the era of Large Language Models (LLMs), Knowledge Distillation (KD) emerges as a pivotal methodology for transferring advanced capabilities from leading proprietary LLMs, such as GPT-4, to their open-source counterparts like LLaMA and Mistral.
Leveraging Large Language Models for NLG Evaluation: A Survey
In the rapidly evolving domain of Natural Language Generation (NLG) evaluation, introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e. g., coherence, creativity, and context relevance.
Re-Reading Improves Reasoning in Large Language Models
To enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs), we introduce a simple, yet general and effective prompting method, Re2, i. e., \textbf{Re}-\textbf{Re}ading the question as input.
Cross-modal Contrastive Learning for Multimodal Fake News Detection
An attention mechanism with an attention guidance module is implemented to help effectively and interpretably aggregate the aligned unimodal representations and the cross-modality correlations.
PoKE: Prior Knowledge Enhanced Emotional Support Conversation with Latent Variable
Further experiments prove that abundant prior knowledge is conducive to high-quality emotional support, and a well-learned latent variable is critical to the diversity of generations.
Subgraph Neighboring Relations Infomax for Inductive Link Prediction on Knowledge Graphs
Inductive link prediction for knowledge graph aims at predicting missing links between unseen entities, those not shown in training stage.
