Search Results for author:
Found 14 papers, 9 papers with code
Towards LLM-RecSys Alignment with Textual ID Learning
The results show that the zero-shot performance of the pre-trained foundation model is comparable to or even better than some traditional recommendation models based on supervised training, showing the potential of the IDGen paradigm serving as the foundation model for generative recommendation.
AIOS: LLM Agent Operating System
Inspired by these challenges, this paper presents AIOS, an LLM agent operating system, which embeds large language model into operating systems (OS) as the brain of the OS, enabling an operating system "with soul" -- an important step towards AGI.
TrustAgent: Towards Safe and Trustworthy LLM-based Agents through Agent Constitution
This paper presents an Agent-Constitution-based agent framework, TrustAgent, an initial investigation into improving the safety dimension of trustworthiness in LLM-based agents.
Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agents
A stack-based LLM plan generation process is then conducted under the supervision of the automaton to ensure that the generated plan satisfies the constraints, making the planning process controllable.
PAP-REC: Personalized Automatic Prompt for Recommendation Language Model
In this paper, we propose PAP-REC, a framework to generate the Personalized Automatic Prompt for RECommendation language models to mitigate the inefficiency and ineffectiveness problems derived from manually designed prompts.
GenRec: Large Language Model for Generative Recommendation
Subsequently, we use these prompts to fine-tune the LLaMA backbone LLM on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics.
Counterfactual Collaborative Reasoning
In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and explainability of machine learning models.
OpenAGI: When LLM Meets Domain Experts
This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents, enabling them to harness expert models for complex task-solving towards Artificial General Intelligence (AGI).
A Survey on Trustworthy Recommender Systems
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process.
AutoLossGen: Automatic Loss Function Generation for Recommender Systems
One challenge for automatic loss generation in recommender systems is the extreme sparsity of recommendation datasets, which leads to the sparse reward problem for loss generation and search.
Explainable Fairness in Recommendation
In this paper, we study the problem of explainable fairness, which helps to gain insights about why a system is fair or unfair, and guides the design of fair recommender systems with a more informed and unified methodology.
From Kepler to Newton: Explainable AI for Science
We show how computational and data-intensive methodology -- together with experimental and theoretical methodology -- can be seamlessly integrated for scientific research.
Counterfactual Evaluation for Explainable AI
While recent years have witnessed the emergence of various explainable methods in machine learning, to what degree the explanations really represent the reasoning process behind the model prediction -- namely, the faithfulness of explanation -- is still an open problem.
Efficient Non-Sampling Knowledge Graph Embedding
Experiments on benchmark datasets show that our NS-KGE framework can achieve a better performance on efficiency and accuracy over traditional negative sampling based models, and that the framework is applicable to a large class of knowledge graph embedding models.
