PIQARD System for Experimenting and Testing Language Models with Prompting Strategies

被引:0
|
作者
Korcz, Marcin [1 ]
Plaskowski, Dawid [1 ]
Politycki, Mateusz [1 ]
Stefanowski, Jerzy [1 ]
Terentowicz, Alex [1 ]
机构
[1] Poznan Univ Tech, Inst Comp Sci, Poznan, Poland
关键词
Large language models; Prompting; Information retrieval;
D O I
10.1007/978-3-031-43430-3_23
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Large Language Models (LLMs) have seen a surge in popularity due to their impressive results in natural language processing tasks, but there are still challenges to be addressed. Prompting in the question is a solution for some of them. In this paper, we present PIQARD, an open-source Python library that allows researchers to experiment with prompting techniques and information retrieval, and combine them with LLMs. This library includes pre-implemented components and also allows users to integrate their own methods.
引用
收藏
页码:320 / 323
页数:4
相关论文
共 50 条
  • [21] How to Optimize Prompting for Large Language Models in Clinical Research
    Lee, Jeong Hyun
    Shin, Jaeseung
    KOREAN JOURNAL OF RADIOLOGY, 2024, 25 (10) : 869 - 873
  • [22] Standardized nomenclature for litigational legal prompting in generative language models
    Sivakumar A.
    Gelman B.
    Simmons R.
    Discover Artificial Intelligence, 2024, 4 (01):
  • [23] Prompting large language models for inner gains in radiology studies
    India, Partha Pratim Ray
    CLINICAL IMAGING, 2025, 120
  • [24] Active Prompting with Chain-of-Thought for Large Language Models
    Diao, Shizhe
    Wang, Pengcheng
    Lin, Yong
    Pan, Rui
    Liu, Xiang
    Zhang, Tong
    PROCEEDINGS OF THE 62ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1: LONG PAPERS, 2024, : 1330 - 1350
  • [25] Do Language Models Enjoy Their Own Stories? Prompting Large Language Models for Automatic Story Evaluation
    Chhun, Cyril
    Suchanek, Fabian M.
    Clavel, Chloe
    TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, 2024, 12 : 1122 - 1142
  • [26] uCAP: An Unsupervised Prompting Method for Vision-Language Models
    Nguyen, A. Tuan
    Tai, Kai Sheng
    Chen, Bor-Chun
    Shukla, Satya Narayan
    Yu, Harichao
    Torr, Philip
    Tian, Tai-Peng
    Lim, Ser-Nam
    COMPUTER VISION - ECCV 2024, PT LXXIV, 2025, 15132 : 425 - 439
  • [27] Guiding Large Language Models via Directional Stimulus Prompting
    Li, Zekun
    Peng, Baolin
    He, Pengcheng
    Galley, Michel
    Gao, Jianfeng
    Yan, Xifeng
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [28] Prompting Visual-Language Models for Efficient Video Understanding
    Ju, Chen
    Han, Tengda
    Zheng, Kunhao
    Zhang, Ya
    Xie, Weidi
    COMPUTER VISION - ECCV 2022, PT XXXV, 2022, 13695 : 105 - 124
  • [29] The Art of Asking: Prompting Large Language Models for Serendipity Recommendations
    Fu, Zhe
    Niu, Xi
    PROCEEDINGS OF THE 2024 ACM SIGIR INTERNATIONAL CONFERENCE ON THE THEORY OF INFORMATION RETRIEVAL, ICTIR 2024, 2024, : 157 - 166
  • [30] Attention Prompting on Image for Large Vision-Language Models
    Yu, Runpeng
    Yu, Weihao
    Wang, Xinchao
    COMPUTER VISION - ECCV 2024, PT XXX, 2025, 15088 : 251 - 268