Feb. 7, 2024, 5:48 a.m. | Adian Liusie Potsawee Manakul Mark J. F. Gales

cs.CL updates on arXiv.org arxiv.org

Current developments in large language models (LLMs) have enabled impressive zero-shot capabilities across various natural language tasks. An interesting application of these systems is in the automated assessment of natural language generation (NLG), a highly challenging area with great practical benefit. In this paper, we explore two options for exploiting the emergent abilities of LLMs for zero-shot NLG assessment: absolute score prediction, and comparative assessment which uses relative comparisons between pairs of candidates. Though comparative assessment has not been extensively …

application assessment automated benefit capabilities cs.cl current evaluation explore language language generation language models large language large language models llm llms natural natural language natural language generation nlg paper practical systems tasks through zero-shot

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 Data Engineer (m/f/d)

@ Project A Ventures | Berlin, Germany

Principle Research Scientist

@ Analog Devices | US, MA, Boston