arXiv:2608. 08069v1 Announce Type: cross Abstract: Foundation models are increasingly reused as software components, making model selection a critical software-engineering decision.
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arXiv:2606. 08926v1 Announce Type: new Abstract: Knowledge graph completion (KGC) models are commonly evaluated using rank-based metrics such as MRR and Hits@K, despite different users often requiring different evaluation perspectives.
The paper investigates how large language models (LLMs) can evaluate explanations in recommender systems. It generates 18 explanation prototypes and has 14 LLMs rate them, comparing the results to human ratings from a user study. Findings show that while LLMs mimic human rating patterns and correlate moderately with human judgments, their absolute agreement is low and varies with model size and evaluation design, leading to four practical recommendations for using LLMs in this context.
The paper introduces SARA, an industrial framework that scales articulated user rationales (AURs) for recommendation systems. It curates a high‑quality AUR dataset from 240 M users, trains a 7B‑parameter MLLM (SARA‑7B) to generate rationales for millions of authors, and integrates these generated rationales into a production ranking model (SARA‑Ranker). Offline and online experiments demonstrate that the system produces more specific, polarity‑consistent rationales and improves user engagement while reducing negative feedback.
arXiv:2606. 10287v1 Announce Type: new Abstract: Evaluating Knowledge Graph Completion (KGC) models remains challenging because standard assessment relies on isolated rank-based metrics such as MRR, Hits$@$k, and Mean Rank, which often produce conflicting model orderings across datasets.
arXiv:2508. 18636v2 Announce Type: replace-cross Abstract: Representing a new paradigm in software distribution, LLM app stores are rapidly emerging, offering users diverse choices for content generation, coding assistance, education, and more.
arXiv:2602. 08873v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now used for academic expert recommendation.