2 papers
cs.CL2026
F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World
Ziyin Zhang, Zihan Liao, Hang Yu +2
We present F2LLM-v2, a new family of general-purpose, multilingual embedding models in 8 distinct sizes ranging from 80M to 14B. Trained on a newly curated composite of 60 million…
cs.AI2025
Establishing Reliability Metrics for Reward Models in Large Language Models
Yizhou Chen, Yawen Liu, Xuesi Wang +5
The reward model (RM) that represents human preferences plays a crucial role in optimizing the outputs of large language models (LLMs), e.g., through reinforcement learning from hu…