Search Results for author:
Found 13 papers, 6 papers with code
RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning
However, existing RAG methods rely primarily on textual semantics and often fail to incorporate the most relevant items, limiting the effectiveness of the systems.
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling
Large language models (LLMs) exhibit varying strengths and weaknesses across different tasks, prompting recent studies to explore the benefits of ensembling models to leverage their complementary advantages.
Privacy in LLM-based Recommendation: Recent Advances and Future Directions
Nowadays, large language models (LLMs) have been integrated with conventional recommendation models to improve recommendation performance.
Learning From Correctness Without Prompting Makes LLM Efficient Reasoner
Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and toxic content.
Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery
We present CNER-UAV, a fine-grained \textbf{C}hinese \textbf{N}ame \textbf{E}ntity \textbf{R}ecognition dataset specifically designed for the task of address resolution in \textbf{U}nmanned \textbf{A}erial \textbf{V}ehicle delivery systems.
Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation
Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior.
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems.
MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning
In this paper, we present a method to fine-tune open-source language models, enabling them to use code for modeling and deriving math equations and, consequently, enhancing their mathematical reasoning abilities.
Ranked #6 on
Math Word Problem Solving
on SVAMP
(using extra training data)
Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification
We found that its success can be largely attributed to its powerful skills in generating and executing code, evaluating the output of code execution, and rectifying its solution when receiving unreasonable outputs.
Ranked #6 on
Math Word Problem Solving
on MATH
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training
Each user learns a personalized model by combining the global federated model, the cluster-level federated model, and its own fine-tuned local model.
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation
Federated recommendations leverage the federated learning (FL) techniques to make privacy-preserving recommendations.
HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations
For example, in a mobile game recommendation, contextual features like locations, battery, and storage levels of mobile devices are frequently drifting over time.
Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation
In this paper, we propose a Graph Neural Network based Personalized Federated Recommendation (PerFedRec) framework via joint representation learning, user clustering, and model adaptation.
