4 citations · 8 across the 11 of their papers we have counts for
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Silicon Bureaucracy and AI Test-Oriented Education: Contamination Sensitivity and Score Confidence in LLM Benchmarks
Yiliang Song, Hongjun An, Jiangan Chen +4
Public benchmarks increasingly govern how large language models (LLMs) are ranked, selected, and deployed. We frame this benchmark-centered regime as Silicon Bureaucracy and AI Tes…
CreditAudit: 2 Dimension for LLM Evaluation and Selection
Yiliang Song, Hongjun An, Jiangong Xiao +3
Leaderboard scores on public benchmarks have been steadily rising and converging, with many frontier language models now separated by only marginal differences. However, these scor…
AI Flow: Perspectives, Scenarios, and Approaches
Hongjun An, Wenhan Hu, Sida Huang +11
Pioneered by the foundational information theory by Claude Shannon and the visionary framework of machine intelligence by Alan Turing, the convergent evolution of information and c…
SentenceVAE: Enable Next-sentence Prediction for Large Language Models with Faster Speed, Higher Accuracy and Longer Context
Hongjun An, Yifan Chen, Zhe Sun +1
Current large language models (LLMs) primarily utilize next-token prediction method for inference, which significantly impedes their processing speed. In this paper, we introduce a…