Search Results for author:
Found 10 papers, 4 papers with code
TnT-LLM: Text Mining at Scale with Large Language Models
Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application.
Leveraging Large Language Models for Hybrid Workplace Decision Support
In the era of hybrid work, LLMs can provide intelligent decision support for workers who are designing their hybrid work plans.
A Literature Review on the Smart Wheelchair Systems
The paper acknowledges the commercial challenges faced, like the limitations of indoor usage and the necessity for user training.
Carpe Diem: On the Evaluation of World Knowledge in Lifelong Language Models
The dynamic nature of knowledge in an ever-changing world presents challenges for language models trained on static data; the model in the real world often requires not only acquiring new knowledge but also overwriting outdated information into updated ones.
HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning
With the proliferation of social media, accurate detection of hate speech has become critical to ensure safety online.
NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models
Structured pruning methods have proven effective in reducing the model size and accelerating inference speed in various network architectures such as Transformers.
NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency
We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data.
MonaCoBERT: Monotonic attention based ConvBERT for Knowledge Tracing
Models that consider both interpretability and the performance improvement have been insufficient.
How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time Augmentation
Next, we show that data augmentation cannot guarantee few-shot performance improvement and investigate the effectiveness of data augmentation based on the intensity of augmentation.
There is no rose without a thorn: Finding weaknesses on BlenderBot 2.0 in terms of Model, Data and User-Centric Approach
BlenderBot 2. 0 is a dialogue model that represents open-domain chatbots by reflecting real-time information and remembering user information for an extended period using an internet search module and multi-session.
