The paper examines how different preprocessing steps in fine‑tuning data influence the performance and fairness of a generative large language model recommender system. Using a controlled pipeline that includes supervised fine‑tuning, catalog‑constrained decoding, and prompt augmentation, the authors evaluate three preprocessing choices—item‑title representation, demographic balancing, and auxiliary item‑knowledge enrichment—on the MovieLens‑1M dataset. Results show that catalog constraints eliminate out‑of‑catalog items, prompt augmentation improves consistency and utility, and that demographic balancing and knowledge enrichment can reduce utility gaps for certain groups while preserving overall performance, though gender balancing may lower utility without improving gender indicators.
By Furkan Yasir Goksu, Yaren Yilmaz, Sule Gunduz Oguducu
arXiv:2512. 07019v3 Announce Type: replace-cross Abstract: The proliferation of Large Language Models (LLMs) necessitates valid evaluation methods to provide guidance for both downstream applications and actionable future improvements.
By Zhiyu Xu, Jia Liu, Yixin Wang, Yuqi Gu
arXiv:2409.00696v4 Announce Type: replace-cross
Abstract: Rating-based human evaluation has become an essential tool to accurately evaluate the impressive performance of large language models (LLMs)....
By Jasper Dekoninck, Maximilian Baader, Martin Vechev
The paper argues that language models’ intransitive preferences arise from multiple internally consistent latent orderings rather than noise around a single ordering. By demonstrating that a single ordering cannot explain observed inconsistencies and introducing a noise‑augmented mixture Bradley‑Terry model, the authors show that mixtures of orderings better capture preference structure across several models and tasks. A case study on Moral Machine dilemmas further illustrates that models can share latent components even when aggregate preferences differ.
By Aviral Chawla, William H. W. Thompson, Jean-Gabriel Young
arXiv:2606. 17165v1 Announce Type: cross Abstract: Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at lower cost.
By Joel Persson, M{\aa}rten Schultzberg, Sebastian Ankargren
arXiv:2608.25200v2 Announce Type: replace-cross
Abstract: We study learning a mixture of $k$ Plackett-Luce models from multi-way ranking responses from annotators that may represent heterogeneous und...
By Dongyue Li, Ziniu Zhang, Lu Wang, Hongyang R. Zhang
PACIFIC is a framework that aligns large language model responses with user preferences by leveraging stable Big‑Five personality traits as a latent signal. The authors built a 1,200‑pair dataset covering diverse domains and trait directions, and found that trait‑aligned contexts enable LLMs to achieve near‑ceiling accuracy (up to 99%) in personalized QA. They also introduced a persona‑aware contrastive retriever (PiRAG) that improves label‑free accuracy from 30% to 43% over standard semantic retrieval, highlighting retrieval as the main bottleneck.
By Tianyu Zhao, Siqi Li, Yasser Shoukry, Salma Elmalaki
arXiv:2607. 16232v1 Announce Type: cross Abstract: The growing use of statistical learning algorithms to infer human preferences from high-dimensional choice data runs up against a fundamental challenge: choice alternatives typically differ in many ways simultaneously, so it is generally unclear which factors actually drove an observed decision and should be credited as preferences.
By Zachary Wojtowicz, Ayush Nayak, Jacob Andreas
arXiv:2601. 02580v2 Announce Type: replace-cross Abstract: Traditional methods for determining assessment item parameters, such as difficulty and discrimination, rely heavily on expensive field testing to collect student performance data for Item Response Theory (IRT) calibration.
By Christopher Ormerod
arXiv:2608.10503v2 Announce Type: replace
Abstract: As Large Language Models (LLMs) are increasingly deployed as autonomous agents, accurately evaluating their latent values and biases is critical. T...
By Davood Wadi, Mohsen Ghodrat, Matthew Philp
arXiv:2607. 17654v1 Announce Type: cross Abstract: Multidimensional graded response models (MGRMs) are widely used for analyzing ordinal questionnaire data in psychological and educational assessments.
By Yu Zhou, Yincai Tang, Bin Lv, Meng Gao
Multidimensional graded response models (MGRMs) are widely used for analyzing ordinal questionnaire data in psychological and educational assessments. A central challenge in applying these models is determining the number of latent dimensions.