34 citations · 91 across the 23 of their papers we have counts for
24 papers
Overview of FinMMEval 2026 Task 2: Multilingual Financial Short-Answer Question Answering
Zhuohan Xie, Xueqing Peng, Georgi Georgiev +18
FinMMEval 2026 Task 2 evaluates short-answer financial question answering over multilingual evidence. Each final-test item pairs an English question with financial statements and n…
Overview of FinMMEval 2026 Task 1: Multilingual Financial Multiple-Choice Question Answering
Zhuohan Xie, Yuyang Dai, Rania Elbadry +18
FinMMEval 2026 Task 1 evaluates multilingual financial multiple-choice question answering in English, Chinese, Arabic, and Hindi. The task tests whether systems can select the corr…
From Noise to Order: Learning to Rank via Denoising Diffusion
Sajad Ebrahimi, Bhaskar Mitra, Negar Arabzadeh +4
In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approaches that model the probability of the…
Privis: Towards Content-Aware Secure Volumetric Video Delivery
Kaiyuan Hu, Hong Kang, Yili Jin +4
Volumetric video has emerged as a key paradigm in eXtended Reality (XR) and immersive multimedia because it enables highly interactive, spatially consistent 3D experiences. However…
Counterfactual Multi-player Bandits for Explainable Recommendation Diversification
Yansen Zhang, Bowei He, Xiaokun Zhang +3
Existing recommender systems tend to prioritize items closely aligned with users' historical interactions, inevitably trapping users in the dilemma of ``filter bubble''. Recent eff…
Understanding 6G through Language Models: A Case Study on LLM-aided Structured Entity Extraction in Telecom Domain
Ye Yuan, Haolun Wu, Hao Zhou +5
Knowledge understanding is a foundational part of envisioned 6G networks to advance network intelligence and AI-native network architectures. In this paradigm, information extracti…
Retrieval-Augmented Generation for Natural Language Processing: A Survey
Shangyu Wu, Ying Xiong, Yufei Cui +8
Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters that store knowledge. However, LLMs still…
Design Editing for Offline Model-based Optimization
Ye Yuan, Youyuan Zhang, Can Chen +5
Offline model-based optimization (MBO) aims to maximize a black-box objective function using only an offline dataset of designs and scores. These tasks span various domains, such a…
Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities
Hao Zhou, Chengming Hu, Ye Yuan +11
Large language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities, leading to great progress in many fiel…
Diffusion-based Contrastive Learning for Sequential Recommendation
Ziqiang Cui, Haolun Wu, Bowei He +2
Contrastive learning has been effectively utilized to enhance the training of sequential recommendation models by leveraging informative self-supervised signals. Most existing appr…
Towards Group-aware Search Success
Haolun Wu, Bhaskar Mitra, Nick Craswell
Traditional measures of search success often overlook the varying information needs of different demographic groups. To address this gap, we introduce a novel metric, named Group-a…
Examining the Role of Peer Acknowledgements on Social Annotations: Unraveling the Psychological Underpinnings
Xiaoshan Huang, Haolun Wu, Xue Liu +1
This study explores the impact of peer acknowledgement on learner engagement and implicit psychological attributes in written annotations on an online social reading platform. Part…
Learning to Extract Structured Entities Using Language Models
Haolun Wu, Ye Yuan, Liana Mikaelyan +4
Recent advances in machine learning have significantly impacted the field of information extraction, with Language Models (LMs) playing a pivotal role in extracting structured info…
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation
Chengming Hu, Haolun Wu, Xuan Li +5
Knowledge distillation aims to train a compact student network using soft supervision from a larger teacher network and hard supervision from ground truths. However, determining an…
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval
Haolun Wu, Ofer Meshi, Masrour Zoghi +4
Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, l…
Teacher-Student Architecture for Knowledge Distillation: A Survey
Chengming Hu, Xuan Li, Dan Liu +4
Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale problems in many areas, such DNNs are hard to be deployed in real-world systems due to their…
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives
Fuyuan Lyu, Xing Tang, Dugang Liu +4
Representation learning has been a critical topic in machine learning. In Click-through Rate Prediction, most features are represented as embedding vectors and learned simultaneous…
Result Diversification in Search and Recommendation: A Survey
Haolun Wu, Yansen Zhang, Chen Ma +4
Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of provide…
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
Haolun Wu, Yingxue Zhang, Chen Ma +4
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations. Many SOTA methods f…
Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation
Chang Meng, Ziqi Zhao, Wei Guo +6
Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users' multi-fac…
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
Haolun Wu, Chen Ma, Yingxue Zhang +3
Implicit feedback is frequently used for developing personalized recommendation services due to its ubiquity and accessibility in real-world systems. In order to effectively utiliz…
Joint Multisided Exposure Fairness for Recommendation
Haolun Wu, Bhaskar Mitra, Chen Ma +2
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of…
Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation
Haolun Wu, Chen Ma, Bhaskar Mitra +2
Nowadays, most online services are hosted on multi-stakeholder marketplaces, where consumers and producers may have different objectives. Conventional recommendation systems, howev…
Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
Chen Ma, Liheng Ma, Yingxue Zhang +3
Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the abi…