April 16, 2024, 4:43 a.m. | V\'ictor A. Braberman, Flavia Bonomo-Braberman, Yiannis Charalambous, Juan G. Colonna, Lucas C. Cordeiro, Rosiane de Freitas

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.09384v1 Announce Type: cross
Abstract: Prompting has become one of the main approaches to leverage emergent capabilities of Large Language Models [Brown et al. NeurIPS 2020, Wei et al. TMLR 2022, Wei et al. NeurIPS 2022]. During the last year, researchers and practitioners have been playing with prompts to see how to make the most of LLMs. By homogeneously dissecting 80 papers, we investigate in deep how software testing and verification research communities have been abstractly architecting their LLM-enabled solutions. …

abstract arxiv become capabilities cs.ai cs.cl cs.lg cs.se language language models large language large language models llm neurips neurips 2022 people prompt prompting researchers software tasks taxonomy type verification

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

C003549 Data Analyst (NS) - MON 13 May

@ EMW, Inc. | Braine-l'Alleud, Wallonia, Belgium

Marketing Decision Scientist

@ Meta | Menlo Park, CA | New York City