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Found 18 papers, 14 papers with code
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
The MAmmoTH models are trained on MathInstruct, our meticulously curated instruction tuning dataset.
Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System
We introduce TacoBot, a user-centered task-oriented digital assistant designed to guide users through complex real-world tasks with multiple steps.
Can ChatGPT Defend the Truth? Automatic Dialectical Evaluation Elicits LLMs' Deficiencies in Reasoning
We explore testing the reasoning ability of large language models (LLMs), such as ChatGPT, by engaging with them in a debate-like conversation that probes deeper into their understanding of the subject.
Automatic Evaluation of Attribution by Large Language Models
To facilitate the evaluation, we manually curate a set of test examples covering 12 domains from a generative search engine, New Bing.
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data.
Bootstrapping a User-Centered Task-Oriented Dialogue System
We present TacoBot, a task-oriented dialogue system built for the inaugural Alexa Prize TaskBot Challenge, which assists users in completing multi-step cooking and home improvement tasks.
C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References
And with our pretrained reader, the entire system improves by up to 4% in exact match.
Synthetic Question Value Estimation for Domain Adaptation of Question Answering
Synthesizing QA pairs with a question generator (QG) on the target domain has become a popular approach for domain adaptation of question answering (QA) models.
Differential Privacy for Text Analytics via Natural Text Sanitization
The sanitized texts also contribute to our sanitization-aware pretraining and fine-tuning, enabling privacy-preserving natural language processing over the BERT language model with promising utility.
CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering
Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts.
COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval
For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1, 236 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query.
PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation
De-identification is the task of identifying protected health information (PHI) in the clinical text.
Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset
In this paper, we provide an in-depth analysis of this dataset and the clinical reading comprehension (CliniRC) task.
Practical Annotation Strategies for Question Answering Datasets
Here, remarkably, annotating a stratified subset with only 1. 2% of the original training set achieves 97. 7% of the performance as if the complete dataset was annotated.
Towards Making the Most of Context in Neural Machine Translation
Document-level machine translation manages to outperform sentence level models by a small margin, but have failed to be widely adopted.
Tensor Decomposition with Relational Constraints for Predicting Multiple Types of MicroRNA-disease Associations
To this end, we innovatively represent miRNA-disease-type triplets as a tensor and introduce Tensor Decomposition methods to solve the prediction task.
SurfCon: Synonym Discovery on Privacy-Aware Clinical Data
To solve the problem, we propose a new framework SurfCon that leverages two important types of information in the privacy-aware clinical data, i. e., the surface form information, and the global context information for synonym discovery.
Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis.
