Popular GitHub Repositories for Preference Aggregation

[CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long*, Luka Rimanic, Ce Zhang, Bo Li

[NeurIPS 2024] SimPO: Simple Preference Optimization with a Reference-Free Reward

(AAAI' 20) A Python Toolbox for Machine Learning Model Combination

RewardBench: the first evaluation tool for reward models.

The official implementation of Self-Play Preference Optimization (SPPO)

[AAAI2024] FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning

? OAT: A research-friendly framework for LLM online alignment, including preference learning, reinforcement learning, etc.

A curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval.

Implementation of Principal Neighbourhood Aggregation for Graph Neural Networks in PyTorch, DGL and PyTorch Geometric

VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation

Better Aligning Text-to-Image Models with Human Preference. ICCV 2023

[ICLR 2024] MogaNet: Efficient Multi-order Gated Aggregation Network

[Paper][ACL 2024 Findings] Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering

Python based Graph Propagation algorithm, DeepWalk to evaluate and compare preference propagation algorithms in heterogeneous information networks from user item relation ship.

[ICCV 2019]Aggregation via Separation: Boosting Facial Landmark Detector with Semi-Supervised Style Transition

4th Place Solution for the Kaggle Competition: LMSYS - Chatbot Arena Human Preference Predictions

Explore concepts like Self-Correct, Self-Refine, Self-Improve, Self-Contradict, Self-Play, and Self-Knowledge, alongside o1-like reasoning elevation? and hallucination alleviation?.

Notus is a collection of fine-tuned LLMs using SFT, DPO, SFT+DPO, and/or any other RLHF techniques, while always keeping a data-first approach

Preference Transformer: Modeling Human Preferences using Transformers for RL (ICLR2023 Accepted)

AAAI 2023 accepted paper, FedALA: Adaptive Local Aggregation for Personalized Federated Learning