Large language models can learn temporal reasoning
While large language models (LLMs) have demonstrated remarkable reasoning capabilities,
they are not without their flaws and inaccuracies. Recent studies have introduced various …
they are not without their flaws and inaccuracies. Recent studies have introduced various …
A survey of large language models for graphs
Graphs are an essential data structure utilized to represent relationships in real-world
scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver …
scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver …
Failure diagnosis in microservice systems: A comprehensive survey and analysis
Widely adopted for their scalability and flexibility, modern microservice systems present
unique failure diagnosis challenges due to their independent deployment and dynamic …
unique failure diagnosis challenges due to their independent deployment and dynamic …
Generate-on-graph: Treat llm as both agent and kg in incomplete knowledge graph question answering
To address the issues of insufficient knowledge and hallucination in Large Language
Models (LLMs), numerous studies have explored integrating LLMs with Knowledge Graphs …
Models (LLMs), numerous studies have explored integrating LLMs with Knowledge Graphs …
QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback-based Self-Correction
Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable
success. However, we find existing methods fall short in terms of reliability and efficiency …
success. However, we find existing methods fall short in terms of reliability and efficiency …
Understanding the interplay between parametric and contextual knowledge for large language models
Large language models (LLMs) encode vast amounts of knowledge during pre-training
(parametric knowledge, or PK) and can further be enhanced by incorporating contextual …
(parametric knowledge, or PK) and can further be enhanced by incorporating contextual …
TimeR4: Time-aware Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering
Abstract Temporal Knowledge Graph Question Answering (TKGQA) aims to answer
temporal questions using knowledge in Temporal Knowledge Graphs (TKGs). Previous …
temporal questions using knowledge in Temporal Knowledge Graphs (TKGs). Previous …
Deliberate reasoning for llms as structure-aware planning with accurate world model
Enhancing the reasoning capabilities of large language models (LLMs) remains a key
challenge, especially for tasks that require complex, multi-step decision-making. Humans …
challenge, especially for tasks that require complex, multi-step decision-making. Humans …
TrustUQA: A Trustful Framework for Unified Structured Data Question Answering
Natural language question answering (QA) over structured data sources such as tables and
knowledge graphs (KGs) have been widely investigated, for example with Large Language …
knowledge graphs (KGs) have been widely investigated, for example with Large Language …
NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models
Graphs are a fundamental data structure for representing relationships in real-world
scenarios. With the success of Large Language Models (LLMs) across various natural …
scenarios. With the success of Large Language Models (LLMs) across various natural …