5 citations · 12 across the 22 of their papers we have counts for
23 papers
GraphRAG-Router: Learning Cost-Efficient Routing over GraphRAGs and LLMs with Reinforcement Learning
Dongzhe Fan, Chuanhao Ji, Zimu Wang +2
Graph-based retrieval-augmented generation (GraphRAG) has recently emerged as a powerful paradigm for knowledge-intensive question answering, especially for tasks that require stru…
NUMINA: A Natural Understanding Benchmark for Multi-dimensional Intelligence and Numerical Reasoning Abilities
Changyu Zeng, Yifan Wang, Zimu Wang +6
Recent advancements in 2D multimodal large language models (MLLMs) have significantly improved performance in vision-language tasks. However, extending these capabilities to 3D env…
WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification
Yiwen Jiang, Deval Mehta, Siyuan Yan +3
Multimodal Large Language Models (MLLMs) have shown promise in visual-textual reasoning, with Multimodal Chain-of-Thought (MCoT) prompting significantly enhancing interpretability.…
MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses
Tong Chen, Zimu Wang, Yiyi Miao +6
Medical fact-checking has become increasingly critical as more individuals seek medical information online. However, existing datasets predominantly focus on human-generated conten…
FinDebate: Multi-Agent Collaborative Intelligence for Financial Analysis
Tianshi Cai, Guanxu Li, Nijia Han +10
We introduce FinDebate, a multi-agent framework for financial analysis, integrating collaborative debate with domain-specific Retrieval-Augmented Generation (RAG). Five specialized…
Benchmarking and Improving LVLMs on Event Extraction from Multimedia Documents
Fuyu Xing, Zimu Wang, Wei Wang +1
The proliferation of multimedia content necessitates the development of effective Multimedia Event Extraction (M2E2) systems. Though Large Vision-Language Models (LVLMs) have shown…
Can GRPO Boost Complex Multimodal Table Understanding?
Xiaoqiang Kang, Shengen Wu, Zimu Wang +7
Existing table understanding methods face challenges due to complex table structures and intricate logical reasoning. While supervised finetuning (SFT) dominates existing research,…
Disentangling the Drivers of LLM Social Conformity: An Uncertainty-Moderated Dual-Process Mechanism
Huixin Zhong, Yanan Liu, Qi Cao +4
As large language models (LLMs) integrate into collaborative teams, their social conformity -- the tendency to align with majority opinions -- has emerged as a key concern. In huma…
FTCFormer: Fuzzy Token Clustering Transformer for Image Classification
Muyi Bao, Changyu Zeng, Yifan Wang +5
Transformer-based deep neural networks have achieved remarkable success across various computer vision tasks, largely attributed to their long-range self-attention mechanism and sc…
Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement
Haotan Guo, Jianfei He, Jiayuan Ma +8
Phonetic Cloaking Replacement (PCR), defined as the deliberate use of homophonic or near-homophonic variants to hide toxic intent, has become a major obstacle to Chinese content mo…
LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research
Shuo Yan, Ruochen Li, Ziming Luo +11
Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery. However, their capability in the fundamental yet crucial task of reprodu…
A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions
Hongbin Na, Yining Hua, Zimu Wang +6
Mental health is increasingly critical in contemporary healthcare, with psychotherapy demanding dynamic, context-sensitive interactions that traditional NLP methods struggle to cap…
Language-based Audio Retrieval with Co-Attention Networks
Haoran Sun, Zimu Wang, Qiuyi Chen +3
In recent years, user-generated audio content has proliferated across various media platforms, creating a growing need for efficient retrieval methods that allow users to search fo…
Template-Driven LLM-Paraphrased Framework for Tabular Math Word Problem Generation
Xiaoqiang Kang, Zimu Wang, Xiaobo Jin +3
Solving tabular math word problems (TMWPs) has become a critical role in evaluating the mathematical reasoning ability of large language models (LLMs), where large-scale TMWP sampl…
Detecting Conversational Mental Manipulation with Intent-Aware Prompting
Jiayuan Ma, Hongbin Na, Zimu Wang +4
Mental manipulation severely undermines mental wellness by covertly and negatively distorting decision-making. While there is an increasing interest in mental health care within th…
Domain-specific Guided Summarization for Mental Health Posts
Lu Qian, Yuqi Wang, Zimu Wang +4
In domain-specific contexts, particularly mental health, abstractive summarization requires advanced techniques adept at handling specialized content to generate domain-relevant an…
Guardians of Discourse: Evaluating LLMs on Multilingual Offensive Language Detection
Jianfei He, Lilin Wang, Jiaying Wang +5
Identifying offensive language is essential for maintaining safety and sustainability in the social media era. Though large language models (LLMs) have demonstrated encouraging pot…
Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering
Zimu Wang, Lei Xia, Wei Wang +1
As an essential task in information extraction (IE), Event-Event Causal Relation Extraction (ECRE) aims to identify and classify the causal relationships between event mentions in…
Thinker-DDM: Modeling Deliberation for Machine Translation with a Drift-Diffusion Process
Hongbin Na, Zimu Wang, Mieradilijiang Maimaiti +4
Large language models (LLMs) have demonstrated promising potential in various downstream tasks, including machine translation. However, prior work on LLM-based machine translation…
Generating Valid and Natural Adversarial Examples with Large Language Models
Zimu Wang, Wei Wang, Qi Chen +2
Deep learning-based natural language processing (NLP) models, particularly pre-trained language models (PLMs), have been revealed to be vulnerable to adversarial attacks. However,…
When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks
Hao Peng, Xiaozhi Wang, Jianhui Chen +8
In-context learning (ICL) has become the default method for using large language models (LLMs), making the exploration of its limitations and understanding the underlying causes cr…
OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding
Hao Peng, Xiaozhi Wang, Feng Yao +5
Event understanding aims at understanding the content and relationship of events within texts, which covers multiple complicated information extraction tasks: event detection, even…
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction
Xiaozhi Wang, Yulin Chen, Ning Ding +9
The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two…