Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning

13 October 2023

Papers citing "Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning"

10 / 10 papers shown

Title
Can LLMs handle WebShell detection? Overcoming Detection Challenges with Behavioral Function-Aware Framework Feijiang Han Jiaming Zhang Chuyi Deng Jianheng Tang Yunhuai Liu 27 0 0 14 Apr 2025
STAYKATE: Hybrid In-Context Example Selection Combining Representativeness Sampling and Retrieval-based Approach -- A Case Study on Science Domains Chencheng Zhu Kazutaka Shimada Tomoki Taniguchi Tomoko Ohkuma 33 0 0 31 Dec 2024
On Calibration of LLM-based Guard Models for Reliable Content Moderation Hongfu Liu Hengguan Huang Hao Wang Xiangming Gu Ye Wang 51 2 0 14 Oct 2024
LLM Task Interference: An Initial Study on the Impact of Task-Switch in Conversational History Akash Gupta Ivaxi Sheth Vyas Raina Mark J. F. Gales Mario Fritz 30 4 0 28 Feb 2024
Automatic Combination of Sample Selection Strategies for Few-Shot Learning Branislav Pecher Ivan Srba M. Bieliková Joaquin Vanschoren 23 1 0 05 Feb 2024
Grimoire is All You Need for Enhancing Large Language Models Ding Chen Shichao Song Qingchen Yu Zhiyu Li Wenjin Wang Feiyu Xiong Bo Tang 25 4 0 07 Jan 2024
Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach Yue Yu Rongzhi Zhang Ran Xu Jieyu Zhang Jiaming Shen Chao Zhang 42 21 0 15 Sep 2022
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity Yao Lu Max Bartolo Alastair Moore Sebastian Riedel Pontus Stenetorp AILaw LRM 274 1,114 0 18 Apr 2021
What Makes Good In-Context Examples for GPT- $3$ ? Jiachang Liu Dinghan Shen Yizhe Zhang Bill Dolan Lawrence Carin Weizhu Chen AAML RALM 275 1,296 0 17 Jan 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 241 1,898 0 31 Dec 2020