Exploring Precision and Recall to assess the quality and diversity of LLMs

16 February 2024

Papers citing "Exploring Precision and Recall to assess the quality and diversity of LLMs"

8 / 8 papers shown

Title
Base Models Beat Aligned Models at Randomness and Creativity Peter West Christopher Potts 34 0 0 30 Apr 2025
Measuring Diversity in Synthetic Datasets Yuchang Zhu Huizhe Zhang Bingzhe Wu Jintang Li Zibin Zheng Peilin Zhao Liang Chen Yatao Bian 90 0 0 12 Feb 2025
Benchmarking Language Model Creativity: A Case Study on Code Generation Yining Lu Dixuan Wang Tianjian Li Dongwei Jiang Daniel Khashabi Meng Jiang Daniel Khashabi LRM 49 10 0 12 Jul 2024
From Distributional to Overton Pluralism: Investigating Large Language Model Alignment Thom Lake Eunsol Choi Greg Durrett 34 9 0 25 Jun 2024
Understanding the Effects of RLHF on LLM Generalisation and Diversity Robert Kirk Ishita Mediratta Christoforos Nalmpantis Jelena Luketina Eric Hambro Edward Grefenstette Roberta Raileanu AI4CE ALM 95 63 0 10 Oct 2023
On the Usefulness of Embeddings, Clusters and Strings for Text Generator Evaluation Tiago Pimentel Clara Meister Ryan Cotterell 27 7 0 31 May 2022
StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets Axel Sauer Katja Schwarz Andreas Geiger 174 354 0 01 Feb 2022
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models Ahmed Alaa B. V. Breugel Evgeny S. Saveliev M. Schaar 36 135 0 17 Feb 2021