all AI news
Efficient LLM Comparative Assessment: a Product of Experts Framework for Pairwise Comparisons
May 10, 2024, 4:46 a.m. | Adian Liusie, Vatsal Raina, Yassir Fathullah, Mark Gales
cs.CL updates on arXiv.org arxiv.org
Abstract: LLM-as-a-judge approaches are a practical and effective way of assessing a range of text tasks, aligning with human judgements especially when applied in a comparative assessment fashion. However, when using pairwise comparisons to rank a set of candidates the computational costs scale quadratically with the number of candidates, which can have practical limitations. This paper introduces a Product of Expert (PoE) framework for efficient LLM Comparative Assessment. Here individual comparisons are considered experts that provide …
abstract arxiv assessment computational costs cs.cl experts fashion framework however human judge llm practical product scale set tasks text type
More from arxiv.org / cs.CL updates on arXiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US