4 papers
ASCIIEval: Benchmarking Models' Visual Perception in Text Strings via ASCII Art
Qi Jia, Xiang Yue, Shanshan Huang +5
Perceiving visual semantics embedded within consecutive characters is a crucial yet under-explored capability for both Large Language Models (LLMs) and Multi-modal Large Language M…
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…
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…
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-…