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Found 13 papers, 2 papers with code
A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks, yet their application to specialized domains remains challenging due to the need for deep expertise.
Benchmarking Large Language Models via Random Variables
LLMs must fully understand the problem-solving process for the original problem to correctly answer RV questions with various combinations of variable values.
CLR-Bench: Evaluating Large Language Models in College-level Reasoning
While emerging benchmarks have been proposed to evaluate LLMs in various domains such as mathematics and computer science, they merely measure the accuracy in terms of the final prediction on multi-choice questions.
Neuro-Symbolic Entity Alignment via Variational Inference
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs.
Graph Anomaly Detection with Noisy Labels by Reinforcement Learning
Specifically, we aim to maximize the performance improvement (AUC) of a base detector by cutting noisy edges approximated through the nodes with high-confidence labels.
Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL
Generating accurate SQL from users' natural language questions (text-to-SQL) remains a long-standing challenge due to the complexities involved in user question understanding, database schema comprehension, and SQL generation.
Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion
It aims to mine latent relation patterns for inductive KG completion.
Entity Alignment with Noisy Annotations from Large Language Models
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs.
Cost-efficient Knowledge-based Question Answering with Large Language Models
We are motivated to combine LLMs and prior small models on knowledge graphs (KGMs) for both inferential accuracy and cost saving.
Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering
Specifically, (i) we propose a two-stage prompting strategy with LLMs to densely embody the image into a scene graph with detailed visual features; (ii) We construct a coupled concept graph by linking the mentioned entities with external facts.
Structure Guided Large Language Model for SQL Generation
Recent advancements in large language models (LLMs) have shown promise in bridging the gap between natural language queries and database management systems, enabling users to interact with databases without the background of SQL.
KnowGPT: Knowledge Graph based Prompting for Large Language Models
However, most state-of-the-art LLMs are closed-source, making it challenging to develop a prompting framework that can efficiently and effectively integrate KGs into LLMs with hard prompts only.
Contrastive Knowledge Graph Error Detection
To this end, we propose a novel framework - ContrAstive knowledge Graph Error Detection (CAGED).
