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Found 34 papers, 13 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.
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.
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.
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Model editing aims to correct outdated or erroneous knowledge in large language models (LLMs) without the need for costly retraining.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.
PE: A Poincare Explanation Method for Fast Text Hierarchy Generation
The research focus has shifted to Hierarchical Attribution (HA) for its ability to model feature interactions.
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.
DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing
The performance of visual SLAM in complex, real-world scenarios is often compromised by unreliable feature extraction and matching when using handcrafted features.
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.
GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark
With a fast developing pace of geographic applications, automatable and intelligent models are essential to be designed to handle the large volume of information.
SelfOdom: Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery
To address this, we propose SelfOdom, a self-supervised dual-network framework that can robustly and consistently learn and generate pose and depth estimates in global scale from monocular images.
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.
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.
TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning
Secondly, on top of the proposed graph transformer, we introduce a two-stream encoder that separately extracts representations from temporal neighborhoods associated with the two interaction nodes and then utilizes a co-attentional transformer to model inter-dependencies at a semantic level.
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.
BiRRE: Learning Bidirectional Residual Relation Embeddings for Supervised Hypernymy Detection
The hypernymy detection task has been addressed under various frameworks.
Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining
In this paper, we propose an effective learning procedure named Meta Fine-Tuning (MFT), served as a meta-learner to solve a group of similar NLP tasks for neural language models.
KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification
We further combine a meta-learning process over the auxiliary task distribution and supervised learning to train the neural lexical relation classifier.
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem
In spite of the success of existing meta reinforcement learning methods, they still have difficulty in learning a meta policy effectively for RL problems with sparse reward.
Learning Robust Representations with Graph Denoising Policy Network
Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing popularity in a variety of graph analysis tasks, including node classification and link prediction.
SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings
Lexical relations describe how meanings of terms relate to each other.
Exploratory Neural Relation Classification for Domain Knowledge Acquisition
The goal is to learn a classifier on pre-defined relations and discover new relations expressed in texts.
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation
Prior relevant studies recommend treatments either use supervised learning (e. g. matching the indicator signal which denotes doctor prescriptions), or reinforcement learning (e. g. maximizing evaluation signal which indicates cumulative reward from survival rates).
Learning Fine-grained Relations from Chinese User Generated Categories
User generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly.
A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances
A taxonomy is a semantic hierarchy, consisting of concepts linked by is-a relations.
Transductive Non-linear Learning for Chinese Hypernym Prediction
Finding the correct hypernyms for entities is essential for taxonomy learning, fine-grained entity categorization, query understanding, etc.
Chinese Hypernym-Hyponym Extraction from User Generated Categories
Hypernym-hyponym ({``}is-a{''}) relations are key components in taxonomies, object hierarchies and knowledge graphs.
A Min-max Cult Algorithm for Graph Partitioning and Data Clustering
In this paper, we propose a new algorithm for graph partitioning with an objective function that follows the min-max clustering principle.
