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cs.CL2025
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…
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.CL2024
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-…