A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following
Junrulu
Junrulu
AI & ML interests
None yet
Recent Activity
commented on
a paper
about 11 hours ago
Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models
upvoted
a
paper
about 12 hours ago
Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models
liked
a model
about 15 hours ago
tencent/Youtu-LLM-2B-Base
Organizations
Eliminating Biased Length Reliance of Direct Preference Optimization via Down-Sampled KL Divergence
-
jiazhengli/Pythia-2.8B-HH-RLHF-Iterative-SamPO
Text Generation • 3B • Updated • 11 -
jiazhengli/Pythia-2.8B-TLDR-Iterative-SamPO
Text Generation • 3B • Updated • 6 -
Junrulu/Llama-3-8B-Instruct-Iterative-SamPO
Text Generation • 8B • Updated • 8 • 1 -
Eliminating Biased Length Reliance of Direct Preference Optimization via Down-Sampled KL Divergence
Paper • 2406.10957 • Published • 2
A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following
Eliminating Biased Length Reliance of Direct Preference Optimization via Down-Sampled KL Divergence
-
jiazhengli/Pythia-2.8B-HH-RLHF-Iterative-SamPO
Text Generation • 3B • Updated • 11 -
jiazhengli/Pythia-2.8B-TLDR-Iterative-SamPO
Text Generation • 3B • Updated • 6 -
Junrulu/Llama-3-8B-Instruct-Iterative-SamPO
Text Generation • 8B • Updated • 8 • 1 -
Eliminating Biased Length Reliance of Direct Preference Optimization via Down-Sampled KL Divergence
Paper • 2406.10957 • Published • 2
Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation