Search Results for author: Kevin Chen-Chuan Chang

Found 40 papers, 21 papers with code

Long-form Question Answering: An Iterative Planning-Retrieval-Generation Approach

no code implementations15 Nov 2023 Pritom Saha Akash, Kashob Kumar Roy, Lucian Popa, Kevin Chen-Chuan Chang

From an extensive experiment on both an open domain and a technical domain QA dataset, we find that our model outperforms the state-of-the-art models on various textual and factual metrics for the LFQA task.

Long Form Question Answering Retrieval

Let the Pretrained Language Models "Imagine" for Short Texts Topic Modeling

no code implementations24 Oct 2023 Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang

Besides, we provide a simple solution extending a neural topic model to reduce the effect of noisy out-of-topics text generation from PLMs.

Text Generation Topic Models

Text Fact Transfer

1 code implementation23 Oct 2023 Nishant Balepur, Jie Huang, Kevin Chen-Chuan Chang

Text style transfer is a prominent task that aims to control the style of text without inherently changing its factual content.

Question Answering Question Generation +4

Ask To The Point: Open-Domain Entity-Centric Question Generation

1 code implementation21 Oct 2023 Yuxiang Liu, Jie Huang, Kevin Chen-Chuan Chang

We introduce a new task called *entity-centric question generation* (ECQG), motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking.

Fact Checking Question Generation +1

Descriptive Knowledge Graph in Biomedical Domain

no code implementations18 Oct 2023 Kerui Zhu, Jie Huang, Kevin Chen-Chuan Chang

We present a novel system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relational knowledge.


TopicAdapt- An Inter-Corpora Topics Adaptation Approach

no code implementations8 Oct 2023 Pritom Saha Akash, Trisha Das, Kevin Chen-Chuan Chang

Topic models are popular statistical tools for detecting latent semantic topics in a text corpus.

Topic Models

GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond

1 code implementation28 Sep 2023 Shen Zheng, Yuyu Zhang, Yijie Zhu, Chenguang Xi, Pengyang Gao, Xun Zhou, Kevin Chen-Chuan Chang

With the rapid advancement of large language models (LLMs), there is a pressing need for a comprehensive evaluation suite to assess their capabilities and limitations.


Citation: A Key to Building Responsible and Accountable Large Language Models

no code implementations5 Jul 2023 Jie Huang, Kevin Chen-Chuan Chang

Despite the complexity of implementing such a citation mechanism, along with the potential pitfalls, we advocate for its development.

Unsupervised Open-domain Keyphrase Generation

no code implementations19 Jun 2023 Lam Thanh Do, Pritom Saha Akash, Kevin Chen-Chuan Chang

To solve this problem, we propose a seq2seq model that consists of two modules, namely \textit{phraseness} and \textit{informativeness} module, both of which can be built in an unsupervised and open-domain fashion.

Informativeness Keyphrase Generation

Mastering the ABCDs of Complex Questions: Answer-Based Claim Decomposition for Fine-grained Self-Evaluation

no code implementations24 May 2023 Nishant Balepur, Jie Huang, Samraj Moorjani, Hari Sundaram, Kevin Chen-Chuan Chang

When answering complex questions, large language models (LLMs) may produce answers that do not satisfy all criteria of the question.

Quantifying Association Capabilities of Large Language Models and Its Implications on Privacy Leakage

no code implementations22 May 2023 Hanyin Shao, Jie Huang, Shen Zheng, Kevin Chen-Chuan Chang

The advancement of large language models (LLMs) brings notable improvements across various applications, while simultaneously raising concerns about potential private data exposure.

CCGen: Explainable Complementary Concept Generation in E-Commerce

no code implementations19 May 2023 Jie Huang, Yifan Gao, Zheng Li, Jingfeng Yang, Yangqiu Song, Chao Zhang, Zining Zhu, Haoming Jiang, Kevin Chen-Chuan Chang, Bing Yin

We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e. g., "Digital Cameras", generating a list of complementary concepts, e. g., 1) Camera Lenses 2) Batteries 3) Camera Cases 4) Memory Cards 5) Battery Chargers.

Why Does ChatGPT Fall Short in Providing Truthful Answers?

no code implementations20 Apr 2023 Shen Zheng, Jie Huang, Kevin Chen-Chuan Chang

To better understand the model's particular weaknesses in providing truthful answers, we embark an in-depth exploration of open-domain question answering.

Memorization Open-Domain Question Answering +1

Towards Reasoning in Large Language Models: A Survey

1 code implementation20 Dec 2022 Jie Huang, Kevin Chen-Chuan Chang

Reasoning is a fundamental aspect of human intelligence that plays a crucial role in activities such as problem solving, decision making, and critical thinking.

Decision Making

DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships

1 code implementation20 Dec 2022 Chenzhengyi Liu, Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang

MoREE consists of a mixture of retrievers model that retrieves diverse context sentences related to the given concepts, and a mixture of generators model that generates diverse sentences based on the retrieved contexts.


VER: Unifying Verbalizing Entities and Relations

1 code implementation20 Nov 2022 Jie Huang, Kevin Chen-Chuan Chang

To know the relationship between two entities, humans tend to create a sentence to connect them.

Coordinated Topic Modeling

no code implementations16 Oct 2022 Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang

It then uses the axes to model a corpus for easily understandable representation.

Can Language Models Be Specific? How?

1 code implementation11 Oct 2022 Jie Huang, Kevin Chen-Chuan Chang, JinJun Xiong, Wen-mei Hwu

We hope this work can bring to awareness the notion of specificity of language models and encourage the research community to further explore this important but understudied problem.

Language Modelling Specificity

DEER: Descriptive Knowledge Graph for Explaining Entity Relationships

1 code implementation21 May 2022 Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang, JinJun Xiong, Wen-mei Hwu

Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships.

BIG-bench Machine Learning Descriptive +3

Domain Representative Keywords Selection: A Probabilistic Approach

1 code implementation Findings (ACL) 2022 Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang, Yunyao Li, Lucian Popa, ChengXiang Zhai

We propose a probabilistic approach to select a subset of a \textit{target domain representative keywords} from a candidate set, contrasting with a context domain.

Exploring Variational Graph Auto-Encoders for Extract Class Refactoring Recommendation

no code implementations16 Mar 2022 Pritom Saha Akash, Kevin Chen-Chuan Chang

Then, the variational graph auto-encoder is used to learn a vector representation for each method.

Understanding Jargon: Combining Extraction and Generation for Definition Modeling

1 code implementation14 Nov 2021 Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang, JinJun Xiong, Wen-mei Hwu

From the composition of this phrase, machines may guess twin prime is a certain kind of prime, but it is still difficult to deduce exactly what twin stands for without additional knowledge.

Text Generation

Open Relation Modeling: Learning to Define Relations between Entities

1 code implementation Findings (ACL) 2022 Jie Huang, Kevin Chen-Chuan Chang, JinJun Xiong, Wen-mei Hwu

Relations between entities can be represented by different instances, e. g., a sentence containing both entities or a fact in a Knowledge Graph (KG).

Open Relation Modeling

Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach

1 code implementation ACL 2021 Jie Huang, Kevin Chen-Chuan Chang, JinJun Xiong, Wen-mei Hwu

To support a fine-grained domain without relying on a matching corpus for supervision, we develop hierarchical core-fringe learning, which learns core and fringe terms jointly in a semi-supervised manner contextualized in the hierarchy of the domain.

Geom-GCN: Geometric Graph Convolutional Networks

2 code implementations ICLR 2020 Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, Bo Yang

From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses.

Node Classification on Non-Homophilic (Heterophilic) Graphs Representation Learning +1

Mining Hidden Populations through Attributed Search

no code implementations11 May 2019 Suhansanu Kumar, Heting Gao, Changyu Wang, Hari Sundaram, Kevin Chen-Chuan Chang

When the property of the target entities is not directly queryable via the API, we refer to the property as `hidden' and the population as a hidden population.

Topological Recurrent Neural Network for Diffusion Prediction

1 code implementation28 Nov 2017 Jia Wang, Vincent W. Zheng, Zemin Liu, Kevin Chen-Chuan Chang

As a result, we introduce a new data model, namely diffusion topologies, to fully describe the cascade structure.

Representation Learning

Motif-based Convolutional Neural Network on Graphs

1 code implementation15 Nov 2017 Aravind Sankar, Xinyang Zhang, Kevin Chen-Chuan Chang

This paper introduces a generalization of Convolutional Neural Networks (CNNs) to graphs with irregular linkage structures, especially heterogeneous graphs with typed nodes and schemas.

General Classification Node Classification +1

CONE: Community Oriented Network Embedding

no code implementations5 Sep 2017 Carl Yang, Hanqing Lu, Kevin Chen-Chuan Chang

It is usually modeled as an unsupervised clustering problem on graphs, based on heuristic assumptions about community characteristics, such as edge density and node homogeneity.

Social and Information Networks Physics and Society

Active Learning for Graph Embedding

1 code implementation15 May 2017 Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang

Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information.

Active Learning Graph Embedding +1

From Community Detection to Community Profiling

no code implementations17 Jan 2017 Hongyun Cai, Vincent W. Zheng, Fanwei Zhu, Kevin Chen-Chuan Chang, Zi Huang

Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links.

Community Detection

From Node Embedding To Community Embedding

2 code implementations31 Oct 2016 Vincent W. Zheng, Sandro Cavallari, Hongyun Cai, Kevin Chen-Chuan Chang, Erik Cambria

Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being "close" on the graph are close too in the low-dimensional space.

Graph Embedding Node Classification

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