Ze Gong

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About

Ze Gong is an emerging researcher at the intersection of artificial intelligence, natural language processing, and automated planning. His work focuses on bridging the gap between human language and machine-interpretable formal representations, a challenge central to making AI systems more accessible and practical in real-world settings. Gong's most notable contribution examines how large language models (LLMs) can be leveraged to translate natural language instructions into structured planning goals, addressing a critical bottleneck in AI planning systems. Published in 2023 and already accumulating 45 citations, this work makes a nuanced and important observation: while LLMs demonstrate impressive NLP capabilities, they struggle with the rigorous logical reasoning required for accurate planning tasks. Rather than treating LLMs as end-to-end solvers, Gong's research explores how they can serve as intelligent translators that feed into dedicated planning frameworks, effectively combining the fluency of modern language models with the precision of formal planning systems. This contribution is particularly timely given the rapid proliferation of LLM-based applications, and it offers valuable guidance to researchers and practitioners seeking to deploy AI planning systems in natural language interfaces. Gong's work represents a thoughtful, critical perspective in an area often dominated by unchecked enthusiasm.