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Found 21 papers, 14 papers with code
Attribution Analysis Meets Model Editing: Advancing Knowledge Correction in Vision Language Models with VisEdit
Recent research discovered that the mid-layer representation of the subject's final token in a prompt has a strong influence on factual predictions, and developed Large Language Model (LLM) editing techniques based on this observation.
Multimodal Label Relevance Ranking via Reinforcement Learning
Conventional multi-label recognition methods often focus on label confidence, frequently overlooking the pivotal role of partial order relations consistent with human preference.
Parameter-Efficient and Memory-Efficient Tuning for Vision Transformer: A Disentangled Approach
The synthesized query equipped with task-specific knowledge serves to extract the useful features for downstream tasks from the intermediate representations of the pre-trained model in a query-only manner.
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning
Knowledge-enhanced pre-trained language models (KEPLMs) leverage relation triples from knowledge graphs (KGs) and integrate these external data sources into language models via self-supervised learning.
UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understanding
Specifically, to retrieve the tokens with similar meanings for the semantic data augmentation across different languages, we propose a sequential clustering process in 3 stages: within a single language, across multiple languages of a language family, and across languages from multiple language families.
On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models
Retrieval augmented generation (RAG) exhibits outstanding performance in promoting the knowledge capabilities of large language models (LLMs) with retrieved documents related to user queries.
DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models
(2) Considering that auxiliary parameters are required to store the knowledge for sequential editing, we construct a new dataset named \textbf{DAFSet}, fulfilling recent, popular, long-tail and robust properties to enhance the generality of sequential editing.
Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning
Model editing aims to correct outdated or erroneous knowledge in large language models (LLMs) without the need for costly retraining.
R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models
The reordering learning process is divided into two steps according to the quality of the generated responses: document order adjustment and document representation enhancement.
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models
KEPLMs are pre-trained models that utilize external knowledge to enhance language understanding.
CIDR: A Cooperative Integrated Dynamic Refining Method for Minimal Feature Removal Problem
The minimal feature removal problem in the post-hoc explanation area aims to identify the minimal feature set (MFS).
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
Reasoning is a distinctive human capacity, enabling us to address complex problems by breaking them down into a series of manageable cognitive steps.
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding
In the experiments, we evaluate KANGAROO over various knowledge-aware and general NLP tasks in both full and few-shot learning settings, outperforming various KEPLM training paradigms performance in closed-domains significantly.
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding
In recent years, vision language pre-training frameworks have made significant progress in natural language processing and computer vision, achieving remarkable performance improvement on various downstream tasks.
Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training
Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguistic knowledge from syntactic or dependency analysis.
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs
As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e. g., Graph Neural Networks (GNNs).
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing
The success of Pre-Trained Models (PTMs) has reshaped the development of Natural Language Processing (NLP).
HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction
In this paper, we propose a hierarchical contrastive learning Framework for Distantly Supervised relation extraction (HiCLRE) to reduce noisy sentences, which integrate the global structural information and local fine-grained interaction.
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding
Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding abilities.
SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining
Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding.
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources
In this paper, we introduce a multi-target MRC task for the medical domain, whose goal is to predict answers to medical questions and the corresponding support sentences from medical information sources simultaneously, in order to ensure the high reliability of medical knowledge serving.
