SOTAVerified

Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 27812790 of 9051 papers

TitleStatusHype
Hands-Free VR0
CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models0
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated DataCode1
Fine-Grained Detoxification via Instance-Level Prefixes for Large Language ModelsCode0
Ten computational challenges in human virome studies0
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control0
DiffuSolve: Diffusion-based Solver for Non-convex Trajectory Optimization0
Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns0
Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich ReasoningCode1
MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation SystemsCode1
Show:102550
← PrevPage 279 of 906Next →

No leaderboard results yet.