Search Results for author:
Found 22 papers, 13 papers with code
Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation
We propose a straightforward approach called Distillation Contrastive Decoding (DCD) to enhance the reasoning capabilities of Large Language Models (LLMs) during inference.
README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP
The advancement in healthcare has shifted focus toward patient-centric approaches, particularly in self-care and patient education, facilitated by access to Electronic Health Records (EHR).
BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing
We also examined whether categories(e. g., QA, IE, and generation) of instructions impact model performance.
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
MTet: Multi-domain Translation for English and Vietnamese
We introduce MTet, the largest publicly available parallel corpus for English-Vietnamese translation.
Ranked #1 on
Machine Translation
on IWSLT2015 English-Vietnamese
(using extra training data)
Enriching Biomedical Knowledge for Low-resource Language Through Large-Scale Translation
Biomedical data and benchmarks are highly valuable yet very limited in low-resource languages other than English such as Vietnamese.
TLETA: Deep Transfer Learning and Integrated Cellular Knowledge for Estimated Time of Arrival Prediction
Importantly, our transfer models only train the last layers to map the transferred knowledge, that reduces the training time significantly.
ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation
In this work, we perform exhaustive experiments on both Vietnamese Abstractive Summarization and Named Entity Recognition, validating the performance of ViT5 against many other pretrained Transformer-based encoder-decoder models.
IoT Data Discovery: Routing Table and Summarization Techniques
Specifically, as the first in the field, this paper investigates routing table designs and various compression techniques to support effective and space-efficient IoT data discovery routing.
MTet: Multi-domain Translation for English-Vietnamese
We are excited to introduce a new larger and better quality Machine Translation dataset, MTet, which stands for Multi-domain Translation for English and VieTnamese.
Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations
In this paper, we approach the task of transfer learning between domains that differ in action spaces.
VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization?
Text summarization is a challenging task within natural language processing that involves text generation from lengthy input sequences.
Into Summarization Techniques for IoT Data Discovery Routing
Also, our approach outperforms DHT based approaches by 2 to 6 folds in terms of latency and traffic.
An Efficient Transformer-Based Model for Vietnamese Punctuation Prediction
In both formal and informal texts, missing punctuation marks make the texts confusing and challenging to read.
SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs
In this paper, we propose SPBERT, a transformer-based language model pre-trained on massive SPARQL query logs.
Hierarchical Transformer Encoders for Vietnamese Spelling Correction
We compare our method with other methods and publicly available systems.
SciFive: a text-to-text transformer model for biomedical literature
In this report, we introduce SciFive, a domain-specific T5 model that has been pre-trained on large biomedical corpora.
Ranked #1 on
Natural Language Inference
on MedNLI
CoTexT: Multi-task Learning with Code-Text Transformer
We train CoTexT on different combinations of available PL corpus including both "bimodal" and "unimodal" data.
Does BLEU Score Work for Code Migration?
In this paper, we conducted an empirical study on BLEU score to (in)validate its suitability for the code migration task due to its inability to reflect the semantics of source code.
Recovering Variable Names for Minified Code with Usage Contexts
JSNeat follows a data-driven approach to recover names by searching for them in a large corpus of open-source JS code.
Detection and Prediction of Users Attitude Based on Real-Time and Batch Sentiment Analysis of Facebook Comments
However, it is important to know the position of a certain user on posts even though the opinion is negative.
