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20172026
most citedSession-based Recommendation with Hypergraph Attention Networks

103 citations · 330 across the 70 of their papers we have counts for

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Showing 2025 · cs.CLShow all

6 papers · 2 filters

cs.CL2025

CHOIR: Collaborative Harmonization fOr Inference Robustness

Xiangjue Dong, Cong Wang, Maria Teleki +2

Persona-assigned Large Language Models (LLMs) can adopt diverse roles, enabling personalized and context-aware reasoning. However, even minor demographic perturbations in personas,…

cs.CL2025

Language Models as Semantic Augmenters for Sequential Recommenders

Mahsa Valizadeh, Xiangjue Dong, Rui Tuo +1

Large Language Models (LLMs) excel at capturing latent semantics and contextual relationships across diverse modalities. However, in modeling user behavior from sequential interact…

cs.CL2025

Z-Scores: A Metric for Linguistically Assessing Disfluency Removal

Maria Teleki, Sai Janjur, Haoran Liu +10

Evaluating disfluency removal in speech requires more than aggregate token-level scores. Traditional word-based metrics such as precision, recall, and F1 (E-Scores) capture overall…

cs.CL2025

Conversational Speech Reveals Structural Robustness Failures in SpeechLLM Backbones

Maria Teleki, Sai Janjur, Haoran Liu +11

LLMs serve as the backbone in SpeechLLMs, yet their behavior on spontaneous conversational input remains poorly understood. Conversational speech contains pervasive disfluencies --…

cs.CL2025

Masculine Defaults via Gendered Discourse in Podcasts and Large Language Models

Maria Teleki, Xiangjue Dong, Haoran Liu +1

Masculine defaults are widely recognized as a significant type of gender bias, but they are often unseen as they are under-researched. Masculine defaults involve three key parts: (…

cs.CL2025★ 1 cited

GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking

Yingjian Chen, Haoran Liu, Yinhong Liu +8

Large language models (LLMs) are widely used, but they often generate subtle factual errors, especially in long-form text. These errors are fatal in some specialized domains such a…