arXiv:2607. 28282v1 Announce Type: cross Abstract: Evaluating the quality and relevance of textual outputs from Large Language Models (LLMs) remains challenging and resource-intensive.
By Bertil Braun, Martin Forell
arXiv:2606. 13221v2 Announce Type: replace Abstract: Evaluating new large language models typically requires costly human annotation campaigns at scale.
By Bora Kargi, David Salinas
arXiv:2601. 21817v2 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on open-ended tasks without ground-truth labels is increasingly done via the LLM-as-a-judge paradigm.
By Mingyuan Xu, Xinzi Tan, Jiawei Wu, Doudou Zhou
arXiv:2605. 15416v2 Announce Type: replace-cross Abstract: Jung et al.
By Gaojie Jin, Yong Tao, Lijia Yu, Tianjin Huang
arXiv:2606. 15029v1 Announce Type: new Abstract: LLM judges are used to reduce the need for costly human labor in evaluating open-ended text generation.
By Alyssa Unell, Natalie Dullerud, Naomi Boneh, Meena Jagadeesan, Tatsu Hashimoto, Nigam Shah, Sanmi Koyejo
arXiv:2410. 13341v4 Announce Type: replace Abstract: High quality annotations are increasingly a bottleneck in the explosively growing machine learning ecosystem.
By Florian E. Dorner, Vivian Y. Nastl, Moritz Hardt