103 citations · 330 across the 70 of their papers we have counts for
18 papers · 1 filter
Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors
Tian Liu, Anwesha Basu, James Caverlee +1
Visual Species Recognition (VSR) is a fundamental task in scientific disciplines that require species-level identification, including ecology, palynology, evolutionary biology, sys…
Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective
Tian Liu, Anwesha Basu, James Caverlee +1
Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as auto-annotation, as it aims to learn a model from a few labeled and abundant unlabeled task-spec…
BI-DCGAN: A Theoretically Grounded Bayesian Framework for Efficient and Diverse GANs
Mahsa Valizadeh, Rui Tuo, James Caverlee
Generative Adversarial Networks (GANs) are proficient at generating synthetic data but continue to suffer from mode collapse, where the generator produces a narrow range of outputs…
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,…
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…
DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management
Kai Yin, Xiangjue Dong, Chengkai Liu +4
Effective and efficient access to relevant information is essential for disaster management. However, no retrieval model is specialized for disaster management, and existing genera…
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…
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 --…
Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning
Shubham Parashar, Shurui Gui, Xiner Li +8
We aim to improve the reasoning capabilities of language models via reinforcement learning (RL). Recent RL post-trained models like DeepSeek-R1 have demonstrated reasoning abilitie…
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management
Kai Yin, Xiangjue Dong, Chengkai Liu +5
Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on…
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: (…
Towards An Efficient LLM Training Paradigm for CTR Prediction
Allen Lin, Renqin Cai, Yun He +5
Large Language Models (LLMs) have demonstrated tremendous potential as the next-generation ranking-based recommendation system. Many recent works have shown that LLMs can significa…
Federated Conversational Recommender System
Allen Lin, Jianling Wang, Ziwei Zhu +1
Conversational Recommender Systems (CRSs) have become increasingly popular as a powerful tool for providing personalized recommendation experiences. By directly engaging with users…
Complex LLM Planning via Automated Heuristics Discovery
Hongyi Ling, Shubham Parashar, Sambhav Khurana +6
We consider enhancing large language models (LLMs) for complex planning tasks. While existing methods allow LLMs to explore intermediate steps to make plans, they either depend on…
Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insights
Shubham Parashar, Blake Olson, Sambhav Khurana +4
We examine the reasoning and planning capabilities of large language models (LLMs) in solving complex tasks. Recent advances in inference-time techniques demonstrate the potential…
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…
Flow Matching for Collaborative Filtering
Chengkai Liu, Yangtian Zhang, Jianling Wang +2
Generative models have shown great promise in collaborative filtering by capturing the underlying distribution of user interests and preferences. However, existing approaches strug…
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Weizhi Zhang, Yuanchen Bei, Liangwei Yang +15
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recomm…