14 citations · 30 across the 8 of their papers we have counts for
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cs.AI2026
From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models
Ling Shi, Xinwei Wu, Xiaohu Zhao +7
While mechanistic interpretability tools like Sparse Autoencoders (SAEs) can uncover meaningful features within Large Language Models (LLMs), a critical gap remains in transforming…
cs.AI2025
Autoformalization in the Era of Large Language Models: A Survey
Ke Weng, Lun Du, Sirui Li +4
Autoformalization, the process of transforming informal mathematical propositions into verifiable formal representations, is a foundational task in automated theorem proving, offer…
cs.AI2023★ 14 cited
GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking
Jiayan Guo, Lun Du, Hengyu Liu +3
Large language models~(LLM) like ChatGPT have become indispensable to artificial general intelligence~(AGI), demonstrating excellent performance in various natural language process…