6 accepted papers
Although large language models (LLMs) have achieved remarkable advancements, their security remains a pressing concern. One major threat is jailbreak attacks, where adversarial prompts bypass model safeguards to generate harmful or objectionable content. Researchers study jailbreak attacks to unders…
Document-level relation extraction (DocRE) aims to extract relations between entities from unstructured document text. Currently, some studies are utilizing logical rules within evidence sentences to enhance the performance of DocRE. However, in cases where the data does not provide specific evidenc…
In recent years, text classification methods based on neural networks and pre-trained models have gained increasing attention and demonstrated excellent performance. However, these methods still have some limitations in practical applications: (1) They typically focus only on the matching similarity…
Multimodal fusion aims to improve the performance of models for applications by extracting and fusing information in different modalities, including texts, images or others. Recent researches have shown that multimodal fusion is beneficial in many multimedia tasks. In this paper, we study typical mu…
Mining structured knowledge from tweets using named entity recognition (NER) can be beneficial for many downstream applications such as recommendation and intention under standing. With tweet posts tending to be multimodal, multimodal named entity recognition (MNER) has attracted more attention. In…
Modality representation learning is an important problem for multimodal sentiment analysis (MSA), since the highly distinguishable representations can contribute to improving the analysis effect. Previous works of MSA have usually focused on internal fusion strategies for different modalities within…