Search Results for author:
Found 17 papers, 14 papers with code
Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling
Finally, we find that learning with stories shows a higher retention rate for non-native speakers in the follow-up assessment.
Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts
Educational materials such as survey articles in specialized fields like computer science traditionally require tremendous expert inputs and are therefore expensive to create and update.
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings
We propose ConGraT(Contrastive Graph-Text pretraining), a general, self-supervised method for jointly learning separate representations of texts and nodes in a parent (or ``supervening'') graph, where each text is associated with one of the nodes.
PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits
Despite the many use cases for large language models (LLMs) in creating personalized chatbots, there has been limited research on evaluating the extent to which the behaviors of personalized LLMs accurately and consistently reflect specific personality traits.
PCB-RandNet: Rethinking Random Sampling for LIDAR Semantic Segmentation in Autonomous Driving Scene
Fast and efficient semantic segmentation of large-scale LiDAR point clouds is a fundamental problem in autonomous driving.
CommunityLM: Probing Partisan Worldviews from Language Models
As political attitudes have diverged ideologically in the United States, political speech has diverged lingusitically.
Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications
Time-trend analysis indicated that Hydroxychloroquine and Ivermectin were discussed more than Molnupiravir and Remdesivir, particularly during COVID-19 surges.
Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis
We release the dataset and make both the Stanza pipeline and BERTweet-based models available "off-the-shelf" for use in future Tweet NLP research.
Ranked #3 on
Dependency Parsing
on Tweebank
Topic Detection and Tracking with Time-Aware Document Embeddings
TDT systems aim to cluster a corpus of news articles by event, and in that context, stories that describe the same event are likely to have been written at around the same time.
Topic-time Heatmaps for Human-in-the-loop Topic Detection and Tracking
The essential task of Topic Detection and Tracking (TDT) is to organize a collection of news media into clusters of stories that pertain to the same real-world event.
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversational systems.
Contrastive Learning of Medical Visual Representations from Paired Images and Text
Existing work commonly relies on fine-tuning weights transferred from ImageNet pretraining, which is suboptimal due to drastically different image characteristics, or rule-based label extraction from the textual report data paired with medical images, which is inaccurate and hard to generalize.
Data augmentation with Mobius transformations
Data augmentation has led to substantial improvements in the performance and generalization of deep models, and remain a highly adaptable method to evolving model architectures and varying amounts of data---in particular, extremely scarce amounts of available training data.
Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings
Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features.
DialectGram: Detecting Dialectal Variation at Multiple Geographic Resolutions
In this work, we propose a model that enables detection of dialectal variation at multiple levels of geographic resolution obviating the need for a-priori definition of the resolution level.
