The paper introduces semanticfa, an R package that performs semantic factor analysis on psychometric scales by converting items into language‑model embeddings and factoring their similarities. It demonstrates the method on the 50‑item International Personality Item Pool Big‑Five markers, compares the semantic factors to a large response‑based factor analysis, and shows how the package can interpret factors, flag redundant items, build short forms, and screen new items. The authors propose semantic‑behavioral coherence as a measurable property of a scale, noting that semantic factor analysis complements but does not replace traditional response‑based factor analysis.
By Devon Yanitski, Chris Westbury
The paper introduces a dual‑dimensional framework called Automated Item Similarity Analysis (AISA) that uses Large Language Models to assess incidental content similarity in large‑scale assessments. It combines Structured Decomposition and Semantic Relatedness to capture both structural and semantic nuances that traditional metrics miss. Psychometric validation shows that LLM‑derived metrics better align with construct‑irrelevant local dependence and produce more coherent item groupings, and simulations in Computerized Adaptive Testing demonstrate improved estimation stability and reduced bias with minimal efficiency loss.
By Jing Huang, Jihong Zhang, Hua-Hua Chang
The paper proposes a model-based evaluation framework that merges multidimensional item response theory (IRT) with question context embeddings to predict large language model (LLM) performance on unseen questions. By representing LLMs with latent capability profiles and incorporating question content to inform item characteristics, the approach improves prediction accuracy over model-free baselines in within-scenario settings and offers a richer description of capability variation than unidimensional models. However, the study also finds that this generalizability does not reliably extend to cross-scenario shifts, indicating a key limitation for broader application.
By Ergan Shang, Weijing Tang, Yinqiu He
HyperPersona is a multi‑level hypergraph framework that models the hierarchical structure of text—document, sentence, and word—using hyperedges for documents and sentences and nodes for words. It employs a transformer‑based graph encoder to capture interactions across these linguistic layers, producing context‑sensitive, structurally grounded features for automatic personality prediction. Experiments on the Big Five personality dimensions demonstrate that HyperPersona, relying solely on text, outperforms state‑of‑the‑art baselines by effectively integrating multi‑level linguistic cues.
By Sina Heydari, Majid Ramezani
The paper introduces the Semantic‑Empirical Updating Framework (SEUF), which uses large language model paraphrases and embeddings aligned with hypothesized item‑factor assignments to create prespecified priors for Bayesian confirmatory factor analysis. In experiments with a 38‑item survey and two public surveys (FEVS and RIASEC), SEUF maintained semantic structure after wording controls and achieved comparable loading precision with 50 respondents to conventional CFA with 70–82 respondents, reducing required sample size by 29–39%. The approach yielded lower loading errors and better credible‑interval coverage, especially when respondent data were scarce, demonstrating the benefit of incorporating semantic priors in small‑sample measurement.
By Shaohuai Feng, Mohd Wira Mohd Shafiei, Mohd Sukri Shafie, Siyang Tian
The paper introduces CHAP, a personalized generative retrieval framework that aligns query semantics with item representations through a hierarchical semantic alignment module and models user behavior using both discrete Semantic IDs and continuous representations. It also proposes a Residual Cascading Generation mechanism to reduce inference latency by limiting the Transformer decoder to a single pass. Experiments on multiple datasets and online A/B tests show that CHAP outperforms existing methods, demonstrating its practical value.
By Gaoming Zhang, Angqing Jiang, Jianchun Song, Kena Qi, Dayao Chen, Wei Lin, Defu Lian
arXiv:2610.33586v1 Announce Type: new
Abstract: Depression severity estimation is crucial for mental health assessment and intervention, yet traditional approaches depend on clinical interviews or se...
By Nada Xu, Xueqin Chen, Qiang Gao, Hongru Liang, Wenqiang Lei
arXiv:2610.09063v1 Announce Type: new
Abstract: Multi-label topic assignment for user-generated content (UGC) -- including product reviews and buyer-seller conversations -- poses unique scalability c...
By Sourabh Kasliwal, Shubhranshu Singh
arXiv:2608. 07881v1 Announce Type: new Abstract: Clustering mixed tabular data requires a unified metric space to bridge the inherent heterogeneity between continuous numerical measurements and discrete categorical symbols.
By Zihua Yang, Zhencheng Xie, Junyang Chen, Liang Xie, Yiqun Zhang, Mengke Li, Yang Lu
arXiv:2608.30044v1 Announce Type: new
Abstract: Language models are commonly compared by averaging scores across a benchmark list with equal weight. Such lists grow through publication outside an exp...
By Jhen-Ke Lin
arXiv:2509. 22888v2 Announce Type: replace Abstract: Standard LLM evaluation practices compress diverse abilities into single scores, obscuring their inherently multidimensional nature.
By Louie Hong Yao, Nicholas Jarvis, Tiffany Zhan, Saptarshi Ghosh, Linfeng Liu, Tianyu Jiang
arXiv:2603. 24226v4 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have inspired a surge of scaling research in industrial search, advertising, and recommendation systems.
By Liren Yu, Caiyuan Li, Feiyi Dong, Tao Zhang, Zhixuan Zhang, Dan Ou, Haihong Tang, Bo Zheng