15 citations · 44 across the 17 of their papers we have counts for
17 papers
A Survey on (M)LLM-Based GUI Agents
Fei Tang, Haolei Xu, Hang Zhang +12
Graphical User Interface (GUI) Agents have emerged as a transformative paradigm in human-computer interaction, evolving from rule-based automation scripts to sophisticated AI-drive…
GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation
Fei Tang, Yongliang Shen, Hang Zhang +6
Large language model-based explainable recommendation (LLM-based ER) systems show promise in generating human-like explanations for recommendations. However, they face challenges i…
EgoSocialArena: Benchmarking the Social Intelligence of Large Language Models from a First-person Perspective
Guiyang Hou, Wenqi Zhang, Yongliang Shen +3
Social intelligence is built upon three foundational pillars: cognitive intelligence, situational intelligence, and behavioral intelligence. As large language models (LLMs) become…
Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model
Wenqi Zhang, Zhenglin Cheng, Yuanyu He +8
Although most current large multimodal models (LMMs) can already understand photos of natural scenes and portraits, their understanding of abstract images, e.g., charts, maps, or l…
Advancing Process Verification for Large Language Models via Tree-Based Preference Learning
Mingqian He, Yongliang Shen, Wenqi Zhang +2
Large Language Models (LLMs) have demonstrated remarkable potential in handling complex reasoning tasks by generating step-by-step rationales.Some methods have proven effective in…
Information Re-Organization Improves Reasoning in Large Language Models
Xiaoxia Cheng, Zeqi Tan, Wei Xue +1
Improving the reasoning capabilities of large language models (LLMs) has attracted considerable interest. Recent approaches primarily focus on improving the reasoning process to yi…
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization
Wenqi Zhang, Ke Tang, Hai Wu +7
Large Language Models (LLMs) exhibit robust problem-solving capabilities for diverse tasks. However, most LLM-based agents are designed as specific task solvers with sophisticated…
MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition
Shuhui Wu, Yongliang Shen, Zeqi Tan +4
Distantly supervised named entity recognition (DS-NER) aims to locate entity mentions and classify their types with only knowledge bases or gazetteers and unlabeled corpus. However…
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks
Xuming Hu, Aiwei Liu, Zeqi Tan +4
Relation extraction (RE) tasks show promising performance in extracting relations from two entities mentioned in sentences, given sufficient annotations available during training.…
PromptNER: Prompt Locating and Typing for Named Entity Recognition
Yongliang Shen, Zeqi Tan, Shuhui Wu +5
Prompt learning is a new paradigm for utilizing pre-trained language models and has achieved great success in many tasks. To adopt prompt learning in the NER task, two kinds of met…
DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition
Zeqi Tan, Shen Huang, Zixia Jia +8
The MultiCoNER \RNum{2} shared task aims to tackle multilingual named entity recognition (NER) in fine-grained and noisy scenarios, and it inherits the semantic ambiguity and low-c…
Query-based Instance Discrimination Network for Relational Triple Extraction
Zeqi Tan, Yongliang Shen, Xuming Hu +4
Joint entity and relation extraction has been a core task in the field of information extraction. Recent approaches usually consider the extraction of relational triples from a ste…
Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem
Wenqi Zhang, Yongliang Shen, Yanna Ma +4
Math word problem solver requires both precise relation reasoning about quantities in the text and reliable generation for the diverse equation. Current sequence-to-tree or relatio…
Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition
Shuhui Wu, Yongliang Shen, Zeqi Tan +1
Nested named entity recognition (nested NER) is a fundamental task in natural language processing. Various span-based methods have been proposed to detect nested entities with span…
Parallel Instance Query Network for Named Entity Recognition
Yongliang Shen, Xiaobin Wang, Zeqi Tan +5
Named entity recognition (NER) is a fundamental task in natural language processing. Recent works treat named entity recognition as a reading comprehension task, constructing type-…
A Sequence-to-Set Network for Nested Named Entity Recognition
Zeqi Tan, Yongliang Shen, Shuai Zhang +2
Named entity recognition (NER) is a widely studied task in natural language processing. Recently, a growing number of studies have focused on the nested NER. The span-based methods…
Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition
Yongliang Shen, Xinyin Ma, Zeqi Tan +3
Named entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-b…