most citedMerge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models

6 citations · 6 across the 5 of their papers we have counts for

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cs.CL2026

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…

cs.CL2024★ 6 cited

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…

cs.CL2024

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…