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20162026
most citedTweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder

134 citations · 706 across the 103 of their papers we have counts for

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Showing 2025Show all

21 papers · 1 filter

cs.LG2025

What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical Reasoning

Yaning Jia, Chunhui Zhang, Xingjian Diao +4

Curriculum learning (CL) - ordering training data from easy to hard - has become a popular strategy for improving reasoning in large language models (LLMs). Yet prior work employs…

cs.CL2025

A Generalizable Rhetorical Strategy Annotation Model Using LLM-based Debate Simulation and Labelling

Shiyu Ji, Farnoosh Hashemi, Joice Chen +9

Rhetorical strategies are central to persuasive communication, from political discourse and marketing to legal argumentation. However, analysis of rhetorical strategies has been li…

cs.CV2025

ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering

Xingjian Diao, Weiyi Wu, Keyi Kong +5

Visual Question Answering (VQA) is increasingly used in diverse applications ranging from general visual reasoning to safety-critical domains such as medical imaging and autonomous…

cs.CL2025

Diffusion Language Models Know the Answer Before Decoding

Pengxiang Li, Yefan Zhou, Dilxat Muhtar +5

Diffusion language models (DLMs) have recently emerged as an alternative to autoregressive approaches, offering parallel sequence generation and flexible token orders. However, the…

cs.IR2025

Non-parametric Graph Convolution for Re-ranking in Recommendation Systems

Zhongyu Ouyang, Mingxuan Ju, Soroush Vosoughi +1

Graph knowledge has been proven effective in enhancing item rankings in recommender systems (RecSys), particularly during the retrieval stage. However, its application in the ranki…

cs.LG2025

KCES: Training-Free Defense for Robust Graph Neural Networks via Kernel Complexity

Yaning Jia, Shenyang Deng, Chiyu Ma +2

Graph Neural Networks (GNNs) have achieved impressive success across a wide range of graph-based tasks, yet they remain highly vulnerable to small, imperceptible perturbations and…

cs.CL2025

DECASTE: Unveiling Caste Stereotypes in Large Language Models through Multi-Dimensional Bias Analysis

Prashanth Vijayaraghavan, Soroush Vosoughi, Lamogha Chiazor +4

Recent advancements in large language models (LLMs) have revolutionized natural language processing (NLP) and expanded their applications across diverse domains. However, despite t…

cs.CL2025

Growing Through Experience: Scaling Episodic Grounding in Language Models

Chunhui Zhang, Sirui, Wang +3

Language models (LMs) require robust episodic grounding-the capacity to learn from and apply past experiences-to excel at physical planning tasks. Current episodic grounding approa…

cs.CL2025

SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models

Xingjian Diao, Chunhui Zhang, Keyi Kong +6

While large language models have demonstrated impressive reasoning abilities, their extension to the audio modality, particularly within large audio-language models (LALMs), remain…

cs.IR2025

What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context

Zhongyu Ouyang, Qianlong Wen, Chunhui Zhang +2

What enables large language models (LLMs) to effectively model user preferences in sequential recommendation? Our investigation reveals that existing preference-alignment approache…

cs.AI2025

Overcoming Multi-step Complexity in Multimodal Theory-of-Mind Reasoning: A Scalable Bayesian Planner

Chunhui Zhang, Zhongyu Ouyang, Kwonjoon Lee +4

Theory-of-Mind (ToM) enables humans to infer mental states-such as beliefs, desires, and intentions-forming the foundation of social cognition. However, existing computational ToM…

cs.CL2025

Probing Association Biases in LLM Moderation Over-Sensitivity

Yuxin Wang, Botao Yu, Ivory Yang +2

Large Language Models are widely used for content moderation but often present certain over-sensitivity, leading to misclassification of benign content and rejecting safe user comm…

cs.SD2025

Music Audio-Visual Question Answering Requires Specialized Multimodal Designs

Wenhao You, Xingjian Diao, Wenjun Huang +9

While recent Multimodal Large Language Models exhibit impressive capabilities for general multimodal tasks, specialized domains like music necessitate tailored approaches. Music Au…

cs.AI2025

Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplifications and Resistance in Multi-Agent Based LLM-as-Judge

Chiyu Ma, Enpei Zhang, Yilun Zhao +7

LLM-as-Judge has emerged as a scalable alternative to human evaluation, enabling large language models (LLMs) to provide reward signals in trainings. While recent work has explored…

cs.LG2025

Scaled Supervision is an Implicit Lipschitz Regularizer

Zhongyu Ouyang, Chunhui Zhang, Yaning Jia +1

In modern social media, recommender systems (RecSys) rely on the click-through rate (CTR) as the standard metric to evaluate user engagement. CTR prediction is traditionally framed…

cs.CL2025

Superficial Self-Improved Reasoners Benefit from Model Merging

Xiangchi Yuan, Chunhui Zhang, Zheyuan Liu +4

As scaled language models (LMs) approach human-level reasoning capabilities, self-improvement emerges as a solution to synthesizing high-quality data corpus. While previous researc…

cs.CV2025

Pretrained Image-Text Models are Secretly Video Captioners

Chunhui Zhang, Yiren Jian, Zhongyu Ouyang +1

Developing video captioning models is computationally expensive. The dynamic nature of video also complicates the design of multimodal models that can effectively caption these seq…

cs.CL2025

Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication

Weicheng Ma, Hefan Zhang, Ivory Yang +8

Large Language Models (LLMs) have shown proficiency in generating persuasive dialogue, yet concerns about the fluency and sophistication of their outputs persist. This paper presen…

cs.CL2025

Is It Navajo? Accurate Language Detection in Endangered Athabaskan Languages

Ivory Yang, Weicheng Ma, Chunhui Zhang +1

Endangered languages, such as Navajo - the most widely spoken Native American language - are significantly underrepresented in contemporary language technologies, exacerbating the…

cs.CV2025

Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding

Xingjian Diao, Chunhui Zhang, Weiyi Wu +5

Multimodal foundation models (MFMs) have demonstrated significant success in tasks such as visual captioning, question answering, and image-text retrieval. However, these models fa…

cs.CL2025

NushuRescue: Revitalization of the Endangered Nushu Language with AI

Ivory Yang, Weicheng Ma, Soroush Vosoughi

The preservation and revitalization of endangered and extinct languages is a meaningful endeavor, conserving cultural heritage while enriching fields like linguistics and anthropol…