15 citations · 21 across the 3 of their papers we have counts for
3 papers
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…
cs.AI2014★ 15 cited
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Patrice Simard, David Chickering, Aparna Lakshmiratan +7
Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine to…