most citedICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems

15 citations · 21 across the 3 of their papers we have counts for

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cs.CL2023★ 2 cited

Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing

Xinyu Hu, Pengfei Tang, Simiao Zuo +5

Large language models (LLMs) have made impressive progress in natural language processing. These models rely on proper human instructions (or prompts) to generate suitable response…

cs.CL2023★ 4 cited

AutoHint: Automatic Prompt Optimization with Hint Generation

Hong Sun, Xue Li, Yinchuan Xu +5

This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM). While LLMs have demonstrated remarkable ability i…