59 citations · 206 across the 26 of their papers we have counts for
27 papers
Grounding Agent Memory in Contextual Intent
Ruozhen Yang, Yucheng Jiang, Yueqi Jiang +3
Deploying large language models in long-horizon, goal-oriented interactions remains challenging because similar entities and facts recur under different latent goals and constraint…
The Kinetics of Reasoning: How Chain-of-Thought Shapes Learning in Transformers?
Zihan Pengmei, Costas Mavromatis, Zhengyuan Shen +3
Chain-of-thought (CoT) supervision can substantially improve transformer performance, yet the mechanisms by which models learn to follow and benefit from CoT remain poorly understo…
TaxoAdapt: Aligning LLM-Based Multidimensional Taxonomy Construction to Evolving Research Corpora
Priyanka Kargupta, Nan Zhang, Yunyi Zhang +3
The rapid evolution of scientific fields introduces challenges in organizing and retrieving scientific literature. While expert-curated taxonomies have traditionally addressed this…
Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking
Yunyi Zhang, Ruozhen Yang, Siqi Jiao +2
Scientific paper retrieval is essential for supporting literature discovery and research. While dense retrieval methods demonstrate effectiveness in general-purpose tasks, they oft…
RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
Pengcheng Jiang, Lang Cao, Ruike Zhu +5
Large language models (LLMs) have achieved impressive performance on knowledge-intensive tasks, yet they often struggle with multi-step reasoning due to the unstructured nature of…
Taxonomy-guided Semantic Indexing for Academic Paper Search
SeongKu Kang, Yunyi Zhang, Pengcheng Jiang +3
Academic paper search is an essential task for efficient literature discovery and scientific advancement. While dense retrieval has advanced various ad-hoc searches, it often strug…
Synergizing Unsupervised Episode Detection with LLMs for Large-Scale News Events
Priyanka Kargupta, Yunyi Zhang, Yizhu Jiao +2
State-of-the-art automatic event detection struggles with interpretability and adaptability to evolving large-scale key events -- unlike episodic structures, which excel in these a…
TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision
Yunyi Zhang, Ruozhen Yang, Xueqiang Xu +4
Hierarchical text classification aims to categorize each document into a set of classes in a label taxonomy, which is a fundamental web text mining task with broad applications suc…
Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains
Yu Zhang, Yunyi Zhang, Yanzhen Shen +5
Accurately typing entity mentions from text segments is a fundamental task for various natural language processing applications. Many previous approaches rely on massive human-anno…
A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion
Yanzhen Shen, Yu Zhang, Yunyi Zhang +1
Entity set expansion, taxonomy expansion, and seed-guided taxonomy construction are three representative tasks that can be applied to automatically populate an existing taxonomy wi…
Ontology Enrichment for Effective Fine-grained Entity Typing
Siru Ouyang, Jiaxin Huang, Pranav Pillai +3
Fine-grained entity typing (FET) is the task of identifying specific entity types at a fine-grained level for entity mentions based on their contextual information. Conventional me…
Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers
Yu Zhang, Bowen Jin, Xiusi Chen +4
Instead of relying on human-annotated training samples to build a classifier, weakly supervised scientific paper classification aims to classify papers only using category descript…
Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic Embedding
Susik Yoon, Dongha Lee, Yunyi Zhang +1
Unsupervised discovery of stories with correlated news articles in real-time helps people digest massive news streams without expensive human annotations. A common approach of the…
PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training
Yunyi Zhang, Minhao Jiang, Yu Meng +2
Weakly-supervised text classification trains a classifier using the label name of each target class as the only supervision, which largely reduces human annotation efforts. Most ex…
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts
Yu Zhang, Yunyi Zhang, Martin Michalski +3
Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract dis…
Entity Set Co-Expansion in StackOverflow
Yu Zhang, Yunyi Zhang, Yucheng Jiang +5
Given a few seed entities of a certain type (e.g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as…
Unsupervised Key Event Detection from Massive Text Corpora
Yunyi Zhang, Fang Guo, Jiaming Shen +1
Automated event detection from news corpora is a crucial task towards mining fast-evolving structured knowledge. As real-world events have different granularities, from the top-lev…
Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations
Yu Meng, Yunyi Zhang, Jiaxin Huang +2
Topic models have been the prominent tools for automatic topic discovery from text corpora. Despite their effectiveness, topic models suffer from several limitations including the…
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training
Yu Meng, Yunyi Zhang, Jiaxin Huang +4
We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw…
Corpus-based Open-Domain Event Type Induction
Jiaming Shen, Yunyi Zhang, Heng Ji +1
Traditional event extraction methods require predefined event types and their corresponding annotations to learn event extractors. These prerequisites are often hard to be satisfie…
Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup
Luyu Gao, Yunyi Zhang, Jiawei Han +1
Contrastive learning has been applied successfully to learn vector representations of text. Previous research demonstrated that learning high-quality representations benefits from…
Text Classification Using Label Names Only: A Language Model Self-Training Approach
Yu Meng, Yunyi Zhang, Jiaxin Huang +4
Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. H…
CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring
Jiaxin Huang, Yiqing Xie, Yu Meng +2
Taxonomy is not only a fundamental form of knowledge representation, but also crucial to vast knowledge-rich applications, such as question answering and web search. Most existing…
Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
Yu Meng, Yunyi Zhang, Jiaxin Huang +3
Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since topic correlations are ubiquitous in massive text corpora. To account for potential hier…