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Evaluating the Ripple Effects of Knowledge Editing in Language Models
Source code for paper "ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance"
This repository contains the implementation of AutoSchemaKG, a novel framework for automatic knowledge graph construction that combines schema generation via conceptualization.
Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment
A modular graph-based Retrieval-Augmented Generation (RAG) system
Document-level Retrieval Augmented Knowledge Graph Construction
[NeurIPS '25] Knowledge Graph Generation from Any Text
Dataset for the paper "GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation"
[EMNLP'25 main] This is the official repo for the paper, Can LLMs be Good Graph Judge for Knowledge Graph Construction?
Code for the arXiv paper: "LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond"
Final Project for Cornell's Introduction to Deep Learning, CS4782
Korean Dense Passage Retrieval (DPR): Semantic Search & RAG
Migrate Freebase entities and relations to Wikidata
[NAACL'24] Evaluate Generative Relation Extraction
[NeurIPS 2024] Official resources of "Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction".
UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection
Integrating Paperpile with Notion Databases 🔄
Evaluate your LLM's response with Prometheus and GPT4 💯
Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach
CoDEx: A set of knowledge graph Completion Datasets Extracted from Wikidata and Wikipedia
Knowledge Graph-Guided Retrieval Augmented Generation, NAACL 2025
[Paper][ACM MM 2024] Making Large Language Models Perform Better in Knowledge Graph Completion
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
[Paper][WWW2025] OntoTune: Ontology-Driven Self-training for Aligning Large Language Models
OpenBioLink is a resource and evaluation framework for evaluating link prediction models on heterogeneous biomedical graph data.
