arXiv:2608. 11947v1 Announce Type: cross Abstract: Multiple-choice benchmarks are widely used to evaluate large language models, but MCQ scores conflate knowledge with sensitivity to option order, which makes them unreliable measures of model knowledge.
By Karl Hanna, Chen Feng
Large language models (LLMs) used for ordinal classification exhibit positional bias, where changes in label order, demonstration order, and demonstration placement affect predictions. Systematic experiments across ten frontier LLMs, eight prompt/task/model factors, and five datasets reveal that all models are sensitive to these positional sources, and that accuracy and stability often diverge. Various correction methods, including pointwise, pairwise, and listwise inference, do not reliably mitigate the bias, though a comparison-based listwise approach shows the best overall balance yet varies across models and bias types.
By Yu Wang, Zhe Zhou, Menglin Liu, Ge Shi
arXiv:2602.02219v3 Announce Type: replace
Abstract: Large language models are widely employed as evaluators, a paradigm commonly referred to as LLM-as-a-judge. Prior research has predominantly examin...
By Yuzheng Xu, Tosho Hirasawa, Tadashi Kozuno, Yoshitaka Ushiku
The paper introduces a lifecycle framework for LLM-as-a-Judge systems used to evaluate recommendation explanations at Netflix. It outlines four phases—Birth, Training, Deployment, and Monitoring—detailing how each stage addresses specific technical and operational challenges. The authors report that after five weeks of A/B testing, judge-aligned explanations increased novel content viewing and successful browse-to-play sessions without quality takedowns.
By Emma Yanyang Kong, JJ Tan, Ishan Gupta, Lars Olds, Claire Campbell, David Fagnan, Veli Balin, Rohan Gosain, Louis Garcia, Minsu Jang
arXiv:2610.01428v1 Announce Type: cross
Abstract: Generalization in large language models (LLMs) is the ability to produce consistent and semantically stable outputs when the same input is expressed...
By Nagham Omar, Mahmoud Jabarin, Maya Rozenshtein, Rom Himelstein, Avi Mendelson, Amit LeVi
arXiv:2607. 04270v1 Announce Type: cross Abstract: Large language models (LLMs) have recently emerged as powerful backbones for recommender systems by reformulating recommendation as a token-level generation task.
By Hongchen Li, Bohao Wang, Jingbang Chen, Weiqin Yang, Hang Pan, Bingde Hu, Can Wang, Jiawei Chen