2 citations · 3 across the 3 of their papers we have counts for
3 papers
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…
cs.CL2024★ 2 cited
MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures
Jinjie Ni, Fuzhao Xue, Xiang Yue +5
Evaluating large language models (LLMs) is challenging. Traditional ground-truth-based benchmarks fail to capture the comprehensiveness and nuance of real-world queries, while LLM-…
cs.LG2020★ 1 cited
S2cGAN: Semi-Supervised Training of Conditional GANs with Fewer Labels
Arunava Chakraborty, Rahul Ragesh, Mahir Shah +1
Generative adversarial networks (GANs) have been remarkably successful in learning complex high dimensional real word distributions and generating realistic samples. However, they…