Zhi Zhang

The paper proposes DP-SUM-CUSUM, a differentially private method for detecting synchronized distributional changes across multiple data streams, explicitly characterizing the privacy-efficiency trade-off.

The paper advocates for integrating explicit contextual feedback (like reviews and comments) into LLM-based recommender systems to achieve more personalized, transparent, and semantically aligned recommendations.

TriLens is a white-box detector that monitors the entropy of three internal streams (attention, feed-forward, residual) at every layer of a language model to detect hallucinations by tracking how internal certainty forms.

QUBRIC introduces a co-design framework that simultaneously optimizes queries and rubrics, overcoming the bottleneck of vague rubrics derived from open-ended questions, leading to significant gains in RL performance.

This paper investigates the distribution and evolution of aspect-level sentiments in peer review comments of accepted papers from Nature Communications, revealing a consistent trend of increasing positive sentiments and decreasing negative sentiments as the number of review rounds increases.

This study constructs and analyzes large-scale algorithm co-occurrence networks in natural language processing using deep learning models.

This paper investigates the correlation between gender diversity and the scientific impact of papers in Natural Language Processing (NLP) and Library and Information Science (LIS) domains.

The paper introduces TikStance, a multimodal and context-aware dataset for stance detection in political discussions on TikTok.

A new framework, PRTA, is proposed for full-ranking recommendation tasks using large language models, where an LLM acts as a central planner and traditional recommendation models perform scoring.