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Found 29 papers, 15 papers with code
Graph Retrieval-Augmented LLM for Conversational Recommendation Systems
Recent Large Language Models (LLMs) demonstrate promising capabilities in natural language understanding and reasoning, showing significant potential for CRSs.
G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
Moreover, existing methods often struggle with the integration of extracted CF information with LLMs due to its implicit representation and the modality gap between graph structures and natural language explanations.
GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation
To address this, we introduce GFM-RAG, a novel graph foundation model (GFM) for retrieval augmented generation.
Unveiling User Preferences: A Knowledge Graph and LLM-Driven Approach for Conversational Recommendation
To address integration challenges, COMPASS employs a two-stage training approach: first, it bridges the gap between the structured KG and natural language through an innovative graph entity captioning pre-training mechanism.
Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models
Large language models (LLMs) have demonstrated impressive reasoning abilities, but they still struggle with faithful reasoning due to knowledge gaps and hallucinations.
Scalable Frame-based Construction of Sociocultural NormBases for Socially-Aware Dialogues
Our empirical results show: (i) the quality of the SCNs derived from synthetic data is comparable to that from real dialogues annotated with gold frames, and (ii) the quality of the SCNs extracted from real data, annotated with either silver (predicted) or gold frames, surpasses that without the frame annotations.
Graph Stochastic Neural Process for Inductive Few-shot Knowledge Graph Completion
Due to the long-tailed distribution of relations and the incompleteness of KGs, there is growing interest in few-shot knowledge graph completion (FKGC).
LLM-Powered Explanations: Unraveling Recommendations Through Subgraph Reasoning
Finally, these reasoning paths are fed into the LLMs to generate interpretable explanations of the recommendation results.
Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning
To account for the evolving nature of TKGs, a dynamic adaptation strategy is proposed to update the LLM-generated rules with the latest events.
Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs
Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought (CoT) explanations alongside answers.
RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations
While collecting sufficient human-authored data is costly, synthetic conversations provide suitable amounts of data to help mitigate the scarcity of training data, as well as the chance to assess the alignment between LLMs and humans in the awareness of social norms.
Continual Learning for Large Language Models: A Survey
Large language models (LLMs) are not amenable to frequent re-training, due to high training costs arising from their massive scale.
SADAS: A Dialogue Assistant System Towards Remediating Norm Violations in Bilingual Socio-Cultural Conversations
We have released our code and software at:~\url{https://github. com/AnonymousEACLDemo/SADAS}.
NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning
NestE represents each atomic fact as a $1\times3$ matrix, and each nested relation is modeled as a $3\times3$ matrix that rotates the $1\times3$ atomic fact matrix through matrix multiplication.
Systematic Assessment of Factual Knowledge in Large Language Models
We systematically evaluate the state-of-the-art LLMs with KGs in generic and specific domains.
Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
In this paper, we propose a novel method called reasoning on graphs (RoG) that synergizes LLMs with KGs to enable faithful and interpretable reasoning.
ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning
Logical rules are essential for uncovering the logical connections between relations, which could improve reasoning performance and provide interpretable results on knowledge graphs (KGs).
A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry.
Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach
Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities.
Unifying Large Language Models and Knowledge Graphs: A Roadmap
In this article, we present a forward-looking roadmap for the unification of LLMs and KGs.
SocialDial: A Benchmark for Socially-Aware Dialogue Systems
To the best of our knowledge, SocialDial is the first socially-aware dialogue dataset that covers multiple social factors and has fine-grained labels.
Cultural Vocal Bursts Intensity Prediction
Synthetic Data Generation
Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion
In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC).
Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs
Specifically, GSNOP combines the advantage of the neural process and neural ordinary differential equation that models the link prediction on dynamic graphs as a dynamic-changing stochastic process.
GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs
Most of existing relevance measures focus on homogeneous networks where objects are of the same type, and a few measures are developed for heterogeneous graphs, but they often need the pre-defined meta-path.
RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on
Virtual try-on(VTON) aims at fitting target clothes to reference person images, which is widely adopted in e-commerce. Existing VTON approaches can be narrowly categorized into Parser-Based(PB) and Parser-Free(PF) by whether relying on the parser information to mask the persons' clothes and synthesize try-on images.
MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation
We integrate these components into a unified framework and present MAMDR, which can be applied to any model structure to perform multi-domain recommendation.
Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model
With the surge of graph embedding mechanism, it has also been adopted to community detection.
RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating
In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation.
Structure Matters: Towards Generating Transferable Adversarial Images
In this paper, we propose the novel concepts of structure patterns and structure-aware perturbations that relax the small perturbation constraint while still keeping images natural.
