110 citations · 112 across the 3 of their papers we have counts for
4 papers
GAOKAO-Eval: Does high scores truly reflect strong capabilities in LLMs?
Zhikai Lei, Tianyi Liang, Hanglei Hu +8
Large Language Models (LLMs) are commonly evaluated using human-crafted benchmarks, under the premise that higher scores implicitly reflect stronger human-like performance. However…
Case2Code: Scalable Synthetic Data for Code Generation
Yunfan Shao, Linyang Li, Yichuan Ma +11
Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. Recent work improves code LLMs by training on synthetic data generated by some powerful LLMs,…
Unified Active Retrieval for Retrieval Augmented Generation
Qinyuan Cheng, Xiaonan Li, Shimin Li +7
In Retrieval-Augmented Generation (RAG), retrieval is not always helpful and applying it to every instruction is sub-optimal. Therefore, determining whether to retrieve is crucial…
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation
Yunfan Shao, Zhichao Geng, Yitao Liu +6
In this paper, we take the advantage of previous pre-trained models (PTMs) and propose a novel Chinese Pre-trained Unbalanced Transformer (CPT). Different from previous Chinese PTM…