AlpacaEval 2.0

'AlpacaEval 2.0' is a dataset/benchmark used to evaluate the alignment of Large Language Models (LLMs) with human preferences through the analysis of generated responses.

Papers using AlpacaEval 2.0 (16)

This Is Your Doge, If It Please You: Exploring Deception And Robustness In Mixture Of Llms2025 · 2 citesLabel-Free Reinforcement Learning via Cross-Model Entropy2026TACOS: Open Tagging and Comparative Scoring for Instruction Fine-Tuning Data Selection2025 · 1 citesMMoA: An AI-Agent framework with recurrence for Memoried Mixure-of-Agent2026S-SPPO: Semantic-Calibrated Self-Play Preference Optimization2026Aligning Large Language Models via Fully Self-Synthetic Data2025Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation2025SGPO: Self-Generated Preference Optimization based on Self-Improver2025Unlocking Recursive Thinking of LLMs: Alignment via Refinement2025Pre-DPO: Improving Data Utilization in Direct Preference Optimization Using a Guiding Reference Model2025MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge2025Temporal Self-Rewarding Language Models: Decoupling Chosen-Rejected via Past-Future2025This Is Your Doge, If It Please You: Exploring Deception and Robustness in Mixture of LLMs2025Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?2025Beyond Sample-Level Feedback: Using Reference-Level Feedback to Guide Data Synthesis2025FocalPO: Enhancing Preference Optimizing by Focusing on Correct Preference Rankings2025