all AI news
OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning
April 2, 2024, 7:53 p.m. | Fei Yu, Anningzhe Gao, Benyou Wang
cs.CL updates on arXiv.org arxiv.org
Abstract: Large language models (LLMs) often struggle with maintaining accuracy throughout multiple multiple reasoning steps, especially in mathematical reasoning where an error in earlier steps can propagate to subsequent ones and it ultimately leading to an incorrect answer. To reduce error propagation, guided decoding is employed to direct the LM decoding on a step-by-step basis. We argue that in guided decoding, assessing the potential of an incomplete reasoning path can be more advantageous than simply ensuring …
abstract accuracy arxiv cs.ai cs.cl decoding error language language models large language large language models llms mathematical reasoning multiple planning propagation reasoning reduce struggle type value
More from arxiv.org / cs.CL updates on arXiv.org
Jobs in AI, ML, Big Data
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 Data Engineer
@ Quantexa | Sydney, New South Wales, Australia
Staff Analytics Engineer
@ Warner Bros. Discovery | NY New York 230 Park Avenue South