3 citations · 5 across the 9 of their papers we have counts for
9 papers
3DCity-LLM: Empowering Multi-modality Large Language Models for 3D City-scale Perception and Understanding
Yiping Chen, Jinpeng Li, Wenyu Ke +6
While multi-modality large language models excel in object-centric or indoor scenarios, scaling them to 3D city-scale environments remains a formidable challenge. To bridge this ga…
TableMind++: An Uncertainty-Aware Programmatic Agent for Tool-Augmented Table Reasoning
Mingyue Cheng, Shuo Yu, Chuang Jiang +5
Table reasoning requires models to jointly perform semantic understanding and precise numerical operations. Most existing methods rely on a single-turn reasoning paradigm over tabl…
TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoning
Chuang Jiang, Mingyue Cheng, Xiaoyu Tao +3
Table reasoning requires models to jointly perform comprehensive semantic understanding and precise numerical operations. Although recent large language model (LLM)-based methods h…
Re3: Learning to Balance Relevance & Recency for Temporal Information Retrieval
Jiawei Cao, Jie Ouyang, Zhaomeng Zhou +4
Temporal Information Retrieval (TIR) is a critical yet unresolved task for modern search systems, retrieving documents that not only satisfy a query's information need but also adh…
am-ELO: A Stable Framework for Arena-based LLM Evaluation
Zirui Liu, Jiatong Li, Yan Zhuang +5
Arena-based evaluation is a fundamental yet significant evaluation paradigm for modern AI models, especially large language models (LLMs). Existing framework based on ELO rating sy…
TextMatch: Enhancing Image-Text Consistency Through Multimodal Optimization
Yucong Luo, Mingyue Cheng, Jie Ouyang +2
Text-to-image generative models excel in creating images from text but struggle with ensuring alignment and consistency between outputs and prompts. This paper introduces TextMatch…
Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation
Yucong Luo, Qitao Qin, Hao Zhang +4
Sequential recommendation (SR) systems have evolved significantly over the past decade, transitioning from traditional collaborative filtering to deep learning approaches and, more…
Revisiting the Solution of Meta KDD Cup 2024: CRAG
Jie Ouyang, Yucong Luo, Mingyue Cheng +4
This paper presents the solution of our team APEX in the Meta KDD CUP 2024: CRAG Comprehensive RAG Benchmark Challenge. The CRAG benchmark addresses the limitations of existing QA…
Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
Shuo Yu, Mingyue Cheng, Qi Liu +6
Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach to mitigating the hallucination of large language models (LLMs) through the integration of…