Search Results for author:
Found 25 papers, 23 papers with code
Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction
Multimodal named entity recognition and relation extraction (MNER and MRE) is a fundamental and crucial branch in information extraction.
CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners
Knowledge Editing (KE) enables the modification of outdated or incorrect information in large language models (LLMs).
How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training
Despite exceptional capabilities in knowledge-intensive tasks, Large Language Models (LLMs) face a critical gap in understanding how they internalize new knowledge, particularly how to structurally embed acquired knowledge in their neural computations.
Exploring Model Kinship for Merging Large Language Models
With comprehensive empirical analysis, we find that there is a certain relationship between model kinship and the performance gains after model merging, which can help guide our selection of candidate models.
OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
Knowledge representation has been a central aim of AI since its inception.
CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs
To address this gap, we introduce CKnowEdit, the first-ever Chinese knowledge editing dataset designed to correct linguistic, factual, and logical errors in LLMs.
Knowledge Mechanisms in Large Language Models: A Survey and Perspective
Understanding knowledge mechanisms in Large Language Models (LLMs) is crucial for advancing towards trustworthy AGI.
Knowledge Circuits in Pretrained Transformers
To date, most studies have concentrated on isolated components within these models, such as the Multilayer Perceptrons and attention head.
WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models
In WISE, we design a dual parametric memory scheme, which consists of the main memory for the pretrained knowledge and a side memory for the edited knowledge.
Detoxifying Large Language Models via Knowledge Editing
This paper investigates using knowledge editing techniques to detoxify Large Language Models (LLMs).
Editing Conceptual Knowledge for Large Language Models
Recently, there has been a growing interest in knowledge editing for Large Language Models (LLMs).
A Comprehensive Study of Knowledge Editing for Large Language Models
In this paper, we first define the knowledge editing problem and then provide a comprehensive review of cutting-edge approaches.
Ranked #1 on
knowledge editing
on zsRE
(using extra training data)
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
This survey addresses the crucial issue of factuality in Large Language Models (LLMs).
Unveiling the Pitfalls of Knowledge Editing for Large Language Models
This paper pioneers the investigation into the potential pitfalls associated with knowledge editing for LLMs.
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data.
LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
We engage in experiments across eight diverse datasets, focusing on four representative tasks encompassing entity and relation extraction, event extraction, link prediction, and question-answering, thereby thoroughly exploring LLMs' performance in the domain of construction and inference.
Editing Large Language Models: Problems, Methods, and Opportunities
Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.
Knowledge Rumination for Pre-trained Language Models
Previous studies have revealed that vanilla pre-trained language models (PLMs) lack the capacity to handle knowledge-intensive NLP tasks alone; thus, several works have attempted to integrate external knowledge into PLMs.
Reasoning with Language Model Prompting: A Survey
Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc.
Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction
With the development of pre-trained language models, many prompt-based approaches to data-efficient knowledge graph construction have been proposed and achieved impressive performance.
Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction
To deal with these issues, we propose a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) for visual-enhanced entity and relation extraction, aiming to achieve more effective and robust performance.
Kformer: Knowledge Injection in Transformer Feed-Forward Layers
In this work, we propose a simple model, Kformer, which takes advantage of the knowledge stored in PTMs and external knowledge via knowledge injection in Transformer FFN layers.
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population.
Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains
In this paper, we present a general approach to developing small, fast and effective pre-trained models for specific domains.
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt).
Ranked #5 on
Dialog Relation Extraction
on DialogRE
(F1 (v1) metric)
