2 papers
cs.CL2024
Boosting LLM via Learning from Data Iteratively and Selectively
Qi Jia, Siyu Ren, Ziheng Qin +3
Datasets nowadays are generally constructed from multiple sources and using different synthetic techniques, making data de-noising and de-duplication crucial before being used for…
cs.AI2024
MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures
Jinjie Ni, Yifan Song, Deepanway Ghosal +10
Perceiving and generating diverse modalities are crucial for AI models to effectively learn from and engage with real-world signals, necessitating reliable evaluations for their de…