all AI news
Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs
Feb. 27, 2024, 5:51 a.m. | Haritz Puerto, Martin Tutek, Somak Aditya, Xiaodan Zhu, Iryna Gurevych
cs.CL updates on arXiv.org arxiv.org
Abstract: Reasoning is a fundamental component of language understanding. Recent prompting techniques, such as chain of thought, have consistently improved LLMs' performance on various reasoning tasks. Nevertheless, there is still little understanding of what triggers reasoning abilities in LLMs in the inference stage. In this paper, we introduce code prompting, a chain of prompts that transforms a natural language problem into code and directly prompts the LLM using the generated code without resorting to external code …
abstract arxiv chain of thought code code llms cs.cl inference language language understanding llms paper performance prompting reasoning stage tasks text thought type understanding
More from arxiv.org / cs.CL updates on arXiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
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
Senior Software Engineer, Generative AI (C++)
@ SoundHound Inc. | Toronto, Canada