Search Results for author:
Found 40 papers, 21 papers with code
Long-form Question Answering: An Iterative Planning-Retrieval-Generation Approach
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.
Let the Pretrained Language Models "Imagine" for Short Texts Topic Modeling
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 Fact Transfer
Text style transfer is a prominent task that aims to control the style of text without inherently changing its factual content.
Ask To The Point: Open-Domain Entity-Centric Question Generation
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.
Descriptive Knowledge Graph in Biomedical Domain
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
Topic models are popular statistical tools for detecting latent semantic topics in a text corpus.
GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond
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.
RAVEN: In-Context Learning with Retrieval Augmented Encoder-Decoder Language Models
In this paper, we investigate the in-context learning ability of retrieval-augmented encoder-decoder language models.
Citation: A Key to Building Responsible and Accountable Large Language Models
Despite the complexity of implementing such a citation mechanism, along with the potential pitfalls, we advocate for its development.
Unsupervised Open-domain Keyphrase Generation
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.
Mastering the ABCDs of Complex Questions: Answer-Based Claim Decomposition for Fine-grained Self-Evaluation
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
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
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.
Expository Text Generation: Imitate, Retrieve, Paraphrase
Expository documents are vital resources for conveying complex information to readers.
Why Does ChatGPT Fall Short in Providing Truthful Answers?
To better understand the model's particular weaknesses in providing truthful answers, we embark an in-depth exploration of open-domain question answering.
Towards Reasoning in Large Language Models: A Survey
Reasoning is a fundamental aspect of human intelligence that plays a crucial role in activities such as problem solving, decision making, and critical thinking.
DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships
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
To know the relationship between two entities, humans tend to create a sentence to connect them.
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications
Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks.
Coordinated Topic Modeling
It then uses the axes to model a corpus for easily understandable representation.
Can Language Models Be Specific? How?
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.
Are Large Pre-Trained Language Models Leaking Your Personal Information?
Are Large Pre-Trained Language Models Leaking Your Personal Information?
DEER: Descriptive Knowledge Graph for Explaining Entity Relationships
Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships.
Domain Representative Keywords Selection: A Probabilistic Approach
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
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
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.
CoVA: Context-aware Visual Attention for Webpage Information Extraction
To address this challenge we propose to reformulate WIE as a context-aware Webpage Object Detection task.
Ranked #1 on
Webpage Object Detection
on CoVA
(using extra training data)
Open Relation Modeling: Learning to Define Relations between Entities
Relations between entities can be represented by different instances, e. g., a sentence containing both entities or a fact in a Knowledge Graph (KG).
Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach
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.
Curvature Regularization to Prevent Distortion in Graph Embedding
Most graph embedding methods preserve the proximity in a graph into a manifold in an embedding space.
Geom-GCN: Geometric Graph Convolutional Networks
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
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
As a result, we introduce a new data model, namely diffusion topologies, to fully describe the cascade structure.
Motif-based Convolutional Neural Network on Graphs
This paper introduces a generalization of Convolutional Neural Networks (CNNs) to graphs with irregular linkage structures, especially heterogeneous graphs with typed nodes and schemas.
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
Graph is an important data representation which appears in a wide diversity of real-world scenarios.
CONE: Community Oriented Network Embedding
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
Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information.
From Community Detection to Community Profiling
Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links.
From Node Embedding To Community Embedding
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.
