Search Results for author:
Found 52 papers, 20 papers with code
Unlocking the `Why' of Buying: Introducing a New Dataset and Benchmark for Purchase Reason and Post-Purchase Experience
To this end, we introduce an LLM-based approach to generate a dataset that consists of textual explanations of why real users make certain purchase decisions.
PRewrite: Prompt Rewriting with Reinforcement Learning
Prompt engineering is critical for the development of LLM-based applications.
Bridging the Preference Gap between Retrievers and LLMs
Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the performance by locating relevant information and placing it into the context window of the LLM.
A Turing Test: Are AI Chatbots Behaviorally Similar to Humans?
Their behaviors are often distinct from average and modal human behaviors, in which case they tend to behave on the more altruistic and cooperative end of the distribution.
Automated Evaluation of Personalized Text Generation using Large Language Models
Personalized text generation presents a specialized mechanism for delivering content that is specific to a user's personal context.
Meta Semantic Template for Evaluation of Large Language Models
We hope this initial work can shed light on future research of LLMs evaluation.
Learning to Rewrite Prompts for Personalized Text Generation
In this paper, we propose a novel method to automatically revise prompts for personalized text generation.
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
We aim to understand why the incorporation of structural information inherent in graph data can improve the prediction performance of LLMs.
Emoji Promotes Developer Participation and Issue Resolution on GitHub
In this paper, we study how emoji usage influences developer participation and issue resolution in virtual workspaces.
Teach LLMs to Personalize -- An Approach inspired by Writing Education
Inspired by the practice of writing education, we develop a multistage and multitask framework to teach LLMs for personalized generation.
Ranking & Reweighting Improves Group Distributional Robustness
Recent work has shown that standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on underrepresented groups due to the prevalence of spurious features.
A Prompt Log Analysis of Text-to-Image Generation Systems
Despite the plenty of efforts to improve the generative models, there is limited work on understanding the information needs of the users of these systems at scale.
Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic
While many organizations have shifted to working remotely during the COVID-19 pandemic, how the remote workforce and the remote teams are influenced by and would respond to this and future shocks remain largely unknown.
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks
First, GLI is designed to incentivize \emph{dataset contributors}.
Why is constrained neural language generation particularly challenging?
Recent advances in deep neural language models combined with the capacity of large scale datasets have accelerated the development of natural language generation systems that produce fluent and coherent texts (to various degrees of success) in a multitude of tasks and application contexts.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Partition-Based Active Learning for Graph Neural Networks
We study the problem of semi-supervised learning with Graph Neural Networks (GNNs) in an active learning setup.
Fast Learning of MNL Model from General Partial Rankings with Application to Network Formation Modeling
The problem of learning mixture of MNL models from partial rankings naturally arises in such applications.
How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules
We further evaluate how well the existing databases and generation models cover the chemical space in terms of #Circles.
Subgroup Generalization and Fairness of Graph Neural Networks
Despite enormous successful applications of graph neural networks (GNNs), theoretical understanding of their generalization ability, especially for node-level tasks where data are not independent and identically-distributed (IID), has been sparse.
Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem
This connection not only enhances our understanding on the problem of adversarial attack on GNNs, but also allows us to propose a group of effective and practical attack strategies.
Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub
This paper studies how emojis, as non-verbal cues in online communications, can be used for such purposes and how the emotional signals in emoji usage can be used to predict future behavior of workers.
Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem
This connection not only enhances our understanding on the problem of adversarial attack on GNNs, but also allows us to propose a group of effective black-box attack strategies.
UMSIForeseer at SemEval-2020 Task 11: Propaganda Detection by Fine-Tuning BERT with Resampling and Ensemble Learning
We describe our participation at the SemEval 2020 {``}Detection of Propaganda Techniques in News Articles{''} - Techniques Classification (TC) task, designed to categorize textual fragments into one of the 14 given propaganda techniques.
CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks
In this paper, we distinguish the \textit{representational} and the \textit{correlational} roles played by the graphs in node-level prediction tasks, and we investigate how Graph Neural Network (GNN) models can effectively leverage both types of information.
SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks
In this paper, we propose a flexible model for survival analysis using neural networks along with scalable optimization algorithms.
Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy
Through interpreting the best-performing models, we discover many novel and actionable insights regarding how to optimize the design and the execution of team competitions on ride-sharing platforms.
Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification
Text simplification reduces the language complexity of professional content for accessibility purposes.
Neural Language Generation: Formulation, Methods, and Evaluation
Nevertheless, there is no standard way to assess the quality of text produced by these generative models, which constitutes a serious bottleneck towards the progress of the field.
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint partitions, but the ranking of items within a partition is unknown.
Towards More Practical Adversarial Attacks on Graph Neural Networks
Our theoretical and empirical analyses suggest that there is a discrepancy between the loss and mis-classification rate, as the latter presents a diminishing-return pattern when the number of attacked nodes increases.
Graph Representation Learning via Multi-task Knowledge Distillation
In this work, we demonstrate that, if available, the domain expertise used for designing handcraft graph features can improve the graph-level representation learning when training labels are scarce.
SEntiMoji: An Emoji-Powered Learning Approach for Sentiment Analysis in Software Engineering
However, commonly used out-of-the-box sentiment analysis tools cannot obtain reliable results on SE tasks and the misunderstanding of technical jargon is demonstrated to be the main reason.
The Strength of the Weakest Supervision: Topic Classification Using Class Labels
When developing topic classifiers for real-world applications, we begin by defining a set of meaningful topic labels.
A Flexible Generative Framework for Graph-based Semi-supervised Learning
In this work, we propose a flexible generative framework for graph-based semi-supervised learning, which approaches the joint distribution of the node features, labels, and the graph structure.
Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation
We conduct a large-scale, systematic study to evaluate the existing evaluation methods for natural language generation in the context of generating online product reviews.
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts
We introduce an adversarial method for producing high-recall explanations of neural text classifier decisions.
Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification
To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i. e., the source language, usually English) to another language with fewer labels (i. e., the target language).
Find the Conversation Killers: a Predictive Study of Thread-ending Posts
In this study, we are particularly interested in identifying a post in a multi-party conversation that is unlikely to be further replied to, which therefore kills that thread of the conversation.
End-to-end Learning for Short Text Expansion
A novel deep memory network is proposed to automatically find relevant information from a collection of longer documents and reformulate the short text through a gating mechanism.
Deep Memory Networks for Attitude Identification
In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets.
Less is More: Learning Prominent and Diverse Topics for Data Summarization
With this reinforced random walk as a general process embedded in classical topic models, we obtain \textit{diverse topic models} that are able to extract the most prominent and diverse topics from data.
Context-aware Natural Language Generation with Recurrent Neural Networks
This paper studied generating natural languages at particular contexts or situations.
Identity-sensitive Word Embedding through Heterogeneous Networks
Based on an identity-labeled text corpora, a heterogeneous network of words and word identities is constructed to model different-levels of word co-occurrences.
DeepCas: an End-to-end Predictor of Information Cascades
While many believe that they are inherently unpredictable, recent work has shown that some key properties of information cascades, such as size, growth, and shape, can be predicted by a machine learning algorithm that combines many features.
DeepGraph: Graph Structure Predicts Network Growth
Conventionally, a graph structure is represented using an adjacency matrix or a set of hand-crafted structural features.
Visualizing Large-scale and High-dimensional Data
We propose the LargeVis, a technique that first constructs an accurately approximated K-nearest neighbor graph from the data and then layouts the graph in the low-dimensional space.
The DARPA Twitter Bot Challenge
A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes.
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
One possible reason is that these text embedding methods learn the representation of text in a fully unsupervised way, without leveraging the labeled information available for the task.
LINE: Large-scale Information Network Embedding
This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.
Ranked #5 on
Node Classification
on Wikipedia
"Look Ma, No Hands!" A Parameter-Free Topic Model
We show that the new parameter can be further eliminated by two parameter-free treatments: either by monitoring the diversity among the discovered topics or by a weak supervision from users in the form of an exemplar topic.
GenDeR: A Generic Diversified Ranking Algorithm
In this paper, we consider a generic setting where we aim to diversify the top-k ranking list based on an arbitrary relevance function and an arbitrary similarity function among all the examples.
