Search Results for author:
Found 13 papers, 5 papers with code
Weakly Supervised Concept Map Generation through Task-Guided Graph Translation
Recent years have witnessed the rapid development of concept map generation techniques due to their advantages in providing well-structured summarization of knowledge from free texts.
Co$^2$PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning
Pre-trained Language Models are widely used in many important real-world applications.
Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media
We present a transformer-based sarcasm detection model that accounts for the context from the entire conversation thread for more robust predictions.
XD at SemEval-2020 Task 12: Ensemble Approach to Offensive Language Identification in Social Media Using Transformer Encoders
This paper presents six document classification models using the latest transformer encoders and a high-performing ensemble model for a task of offensive language identification in social media.
Emora: An Inquisitive Social Chatbot Who Cares For You
Inspired by studies on the overwhelming presence of experience-sharing in human-human conversations, Emora, the social chatbot developed by Emory University, aims to bring such experience-focused interaction to the current field of conversational AI.
Benchmarking of Transformer-Based Pre-Trained Models on Social Media Text Classification Datasets
We compare three pre-trained language models, RoBERTa-base, BERTweet and ClinicalBioBERT in terms of classification accuracy.
Closed-book Question Generation via Contrastive Learning
Through experiments, we validate the proposed QG model on both public datasets and a new WikiCQA dataset.
Ranked #2 on
Open-Domain Question Answering
on ELI5
PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts
A key component of modern conversational systems is the Dialogue State Tracker (or DST), which models a user's goals and needs.
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks
As powerful tools for representation learning on graphs, graph neural networks (GNNs) have played an important role in applications including social networks, recommendation systems, and online web services.
Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation
Large Language Models (LLMs) can generate biased and toxic responses.
DALA: A Distribution-Aware LoRA-Based Adversarial Attack against Language Models
DALA considers distribution shifts of adversarial examples to improve the attack's effectiveness under detection methods.
The Neglected Tails of Vision-Language Models
We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts.
Disclosure and Mitigation of Gender Bias in LLMs
Our experiments demonstrate that all tested LLMs exhibit explicit and/or implicit gender bias, even when gender stereotypes are not present in the inputs.
