6 citations · 9 across the 6 of their papers we have counts for
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cs.AI2026
One Prompt Does Not Fit All: Self-Meta-Evolve for Personalized Information Extraction
Hongliang Li, Lu Wang, Yong Xu +6
Large language models (LLMs) are increasingly deployed for enterprise information extraction (IE), where the same document must be reorganized differently for each user. Existing p…
cs.AI2023★ 6 cited
TaskWeaver: A Code-First Agent Framework
Bo Qiao, Liqun Li, Xu Zhang +16
Large Language Models (LLMs) have shown impressive abilities in natural language understanding and generation, leading to their widespread use in applications such as chatbots and…
cs.AI2023★ 1 cited
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
Ruomeng Ding, Chaoyun Zhang, Lu Wang +7
Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable language sequences referred to as "th…