Search Results for author:
Found 15 papers, 4 papers with code
LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model
Furthermore, we utilize the LLM to enrich the information of personality labels for enhancing the detection performance.
KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge Bases
Secondly, KB-Plugin utilizes abundant annotated data from a rich-resourced KB to train another pluggable module, namely PI plugin, which can help the LLM extract question-relevant schema information from the schema plugin of any KB and utilize this information to induce programs over this KB.
Valley: Video Assistant with Large Language model Enhanced abilitY
Large language models (LLMs), with their remarkable conversational capabilities, have demonstrated impressive performance across various applications and have emerged as formidable AI assistants.
Enhancing Human Capabilities through Symbiotic Artificial Intelligence with Shared Sensory Experiences
In this paper, we introduce a novel concept in Human-AI interaction called Symbiotic Artificial Intelligence with Shared Sensory Experiences (SAISSE), which aims to establish a mutually beneficial relationship between AI systems and human users through shared sensory experiences.
ChatLLM Network: More brains, More intelligence
Dialogue-based language models mark a huge milestone in the field of artificial intelligence, by their impressive ability to interact with users, as well as a series of challenging tasks prompted by customized instructions.
Multimodal Matching-aware Co-attention Networks with Mutual Knowledge Distillation for Fake News Detection
Additionally, based on the designed image-text matching-aware co-attention mechanism, we propose to build two co-attention networks respectively centered on text and image for mutual knowledge distillation to improve fake news detection.
A Survey of Knowledge Enhanced Pre-trained Language Models
We introduce appropriate taxonomies respectively for Natural Language Understanding (NLU) and Natural Language Generation (NLG) to highlight these two main tasks of NLP.
Multimodal Dialog Systems with Dual Knowledge-enhanced Generative Pretrained Language Model
To address these limitations, we propose a novel dual knowledge-enhanced generative pretrained language model for multimodal task-oriented dialog systems (DKMD), consisting of three key components: dual knowledge selection, dual knowledge-enhanced context learning, and knowledge-enhanced response generation.
Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge
Specifically, we first construct a \textit{directed heterogeneous document graph} for each news incorporating topics and entities.
Graph Neural News Recommendation with Unsupervised Preference Disentanglement
Furthermore, the learned representations are disentangled with latent preference factors by a neighborhood routing algorithm, which can enhance expressiveness and interpretability.
Improving Distantly-Supervised Relation Extraction with Joint Label Embedding
Distantly-supervised relation extraction has proven to be effective to find relational facts from texts.
Graph Neural News Recommendation with Long-term and Short-term Interest Modeling
Existing methods on news recommendation mainly include collaborative filtering methods which rely on direct user-item interactions and content based methods which characterize the content of user reading history.
Relation Structure-Aware Heterogeneous Information Network Embedding
In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE).
