arXiv Machine Learning June 16, 2026 By Xuran Li, Guanqin Zhang, Imran Razzak, Hakim Hacid, Eleanna Kafeza, Hao Xue, Flora D. Salim

Evaluating LLM Personalization via Semantic Constraint Verification

Read the original on arXiv Machine Learning →

arXiv:2606. 16368v1 Announce Type: cross Abstract: Current evaluation paradigms for Large Language Model (LLM) personalization rely heavily on brittle surface-matching metrics or computationally expensive LLM-as-a-judge protocols, both of which lack interpretability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Sep 1

Towards Natural Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions

arXiv:2603.04191v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactio...

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Aug 13

Commonsense on Demand: Generating and Selectively Integrating Commonsense Knowledge for Natural Language Inference

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Behaviorally Grounded User Profiles from the Wild for Personalized Alignment and Multi-Perspective Reasoning

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arXiv AI

Aug 7

Cautious Context Steering for Language Model Personalization

arXiv:2608. 05813v1 Announce Type: new Abstract: Personalizing language models (LMs) to individual user preferences is essential for aligning responses with diverse goals and backgrounds.

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Fixing FOLIO and MALLS: Verified Annotations and an LLM-assisted Framework to Focus Human Relabeling

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