6 citations · 6 across the 5 of their papers we have counts for
5 papers
SLATE: Are AI-Generated Slides Educationally Effective? A Benchmark for Language Teaching Quality and Learner Knowledge Acquisition
Jingzhuo Wu, Jiajun Zhang, Liu Yi +4
LLMs have achieved remarkable capabilities in generating language teaching slides. However, a critical mismatch persists between visual polish and actual instructional effectivenes…
Gradients Know What Outcomes Don't: Unlocking Reinforcement Learning for LLM Reasoning with Gradient-Aligned Rewards
Leqi Zheng, Jinbo Su, Fang Niu +8
Reinforcement learning from verifiable rewards (RLVR) drives chain-of-thought reasoning in large language models, yet its binary outcome reward cannot distinguish among correct tra…
Learning to Model Optimization Problems from Solver Outcomes
Runda Guan, Xiangqing Shen, Jiajun Zhang +3
Automating optimization modeling with LLMs is a promising path toward scalable decision intelligence, but existing approaches either rely on agentic pipelines built on closed-sourc…
Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models
Jinliang Lu, Ziliang Pang, Min Xiao +3
The remarkable success of Large Language Models (LLMs) has ushered natural language processing (NLP) research into a new era. Despite their diverse capabilities, LLMs trained on di…
DPPA: Pruning Method for Large Language Model to Model Merging
Yaochen Zhu, Rui Xia, Jiajun Zhang
Model merging is to combine fine-tuned models derived from multiple domains, with the intent of enhancing the model's proficiency across various domains. The principal concern is t…