Search Results for author:
Found 11 papers, 4 papers with code
DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level
Recommendation systems play a crucial role in various domains, suggesting items based on user behavior. However, the lack of transparency in presenting recommendations can lead to user confusion.
360°REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent System
The framework employs a novel 360{\deg} performance assessment method for multi-perspective performance evaluation with fine-grained assessment.
Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering
Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in information systems.
"In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning
Personalized dialogue systems have gained significant attention in recent years for their ability to generate responses in alignment with different personas.
Not all Layers of LLMs are Necessary during Inference
To answer this question, we first indicate that Not all Layers are Necessary during Inference by statistically analyzing the activated layers across tasks.
Explainable Session-based Recommendation via Path Reasoning
This paper explores providing explainability for session-based recommendation (SR) by path reasoning.
From Indeterminacy to Determinacy: Augmenting Logical Reasoning Capabilities with Large Language Models
To address these challenges, we propose DetermLR, a novel reasoning framework that formulates the reasoning process as a transformational journey from indeterminate premises to determinate ones.
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features.
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed.
CharacterChat: Learning towards Conversational AI with Personalized Social Support
In our modern, fast-paced, and interconnected world, the importance of mental well-being has grown into a matter of great urgency.
nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales
With around 14% of the one-time pre-training cost, we can accurately forecast the loss for models up to 52B.
