3 papers
cs.CL2024
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…
cs.CL2024
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,…
cs.CL2024
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…