4 citations · 15 across the 27 of their papers we have counts for
7 papers · 1 filter
Prompting with Phonemes: Enhancing LLMs' Multilinguality for Non-Latin Script Languages
Hoang H Nguyen, Khyati Mahajan, Vikas Yadav +4
Although multilingual LLMs have achieved remarkable performance across benchmarks, we find they continue to underperform on non-Latin script languages across contemporary LLM famil…
Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Data
Hengrui Zhang, Liancheng Fang, Qitian Wu +1
Autoregressive models are predominant in natural language generation, while their application in tabular data remains underexplored. We posit that this can be attributed to two fac…
Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems (RecSys) have been persistent concerns, hindering their deployment in real-world…
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
Jiangshu Du, Yibo Wang, Wenting Zhao +37
This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and ques…
DiffPuter: Empowering Diffusion Models for Missing Data Imputation
Hengrui Zhang, Liancheng Fang, Qitian Wu +1
Generative models play an important role in missing data imputation in that they aim to learn the joint distribution of full data. However, applying advanced deep generative models…
Mixed Supervised Graph Contrastive Learning for Recommendation
Weizhi Zhang, Liangwei Yang, Zihe Song +4
Recommender systems (RecSys) play a vital role in online platforms, offering users personalized suggestions amidst vast information. Graph contrastive learning aims to learn from h…
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction
Henry Peng Zou, Vinay Samuel, Yue Zhou +5
Existing datasets for attribute value extraction (AVE) predominantly focus on explicit attribute values while neglecting the implicit ones, lack product images, are often not publi…