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Found 7 papers, 4 papers with code
Uncertainty Quantification and Decomposition for LLM-based Recommendation
Despite the widespread adoption of large language models (LLMs) for recommendation, we demonstrate that LLMs often exhibit uncertainty in their recommendations.
Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection
In this paper, we propose DynaCor framework that distinguishes incorrectly labeled instances from correctly labeled ones based on the dynamics of the training signals.
Multi-Domain Recommendation to Attract Users via Domain Preference Modeling
Targeting a platform that operates multiple service domains, we introduce a new task, Multi-Domain Recommendation to Attract Users (MDRAU), which recommends items from multiple ``unseen'' domains with which each user has not interacted yet, by using knowledge from the user's ``seen'' domains.
Exploring Language Model's Code Generation Ability with Auxiliary Functions
However, our analysis also reveals the model's underutilized behavior to call the auxiliary function, suggesting the future direction to enhance their implementation by eliciting the auxiliary function call ability encoded in the models.
Rectifying Demonstration Shortcut in In-Context Learning
However, LLMs often rely on their pre-trained semantic priors of demonstrations rather than on the input-label relationships to proceed with ICL prediction.
Top-Personalized-K Recommendation
To address this issue, we introduce Top-Personalized-K Recommendation, a new recommendation task aimed at generating a personalized-sized ranking list to maximize individual user satisfaction.
Tag Embedding and Well-defined Intermediate Representation improve Auto-Formulation of Problem Description
In this report, I address auto-formulation of problem description, the task of converting an optimization problem into a canonical representation.
