3 papers
cs.CL2026
Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains
Zhonghang Yuan, Zhefan Wang, Fang Hu +7
Reinforcement learning with verifiable rewards (RLVR) has demonstrated promising potential to enhance the reasoning capabilities of large language models (LLMs) in domains such as…
cs.CL2025
GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
Zihong Chen, Wanli Jiang, Jinzhe Li +4
Fine-tuning for large language models (LLMs) typically requires substantial amounts of high-quality supervised data, which is both costly and labor-intensive to acquire. While synt…
cs.CL2025
SeedBench: A Multi-task Benchmark for Evaluating Large Language Models in Seed Science
Jie Ying, Zihong Chen, Zhefan Wang +7
Seed science is essential for modern agriculture, directly influencing crop yields and global food security. However, challenges such as interdisciplinary complexity and high costs…