Search Results for author:
Found 7 papers, 5 papers with code
UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor
However, the trained scoring model is prone to under-fitting for low-resource settings, as it relies on the training data.
CoUDA: Coherence Evaluation via Unified Data Augmentation
Due to the scarcity of annotated data, data augmentation is commonly used for training coherence evaluation models.
Reducing Hallucinations in Entity Abstract Summarization with Facts-Template Decomposition
Entity abstract summarization aims to generate a coherent description of a given entity based on a set of relevant Internet documents.
Language Models Understand Numbers, at Least Partially
Large language models (LLMs) have exhibited impressive competence in various tasks, but their opaque internal mechanisms hinder their use in mathematical problems.
Learn to Not Link: Exploring NIL Prediction in Entity Linking
We conduct a series of experiments with the widely used bi-encoder and cross-encoder entity linking models, results show that both types of NIL mentions in training data have a significant influence on the accuracy of NIL prediction.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
TWAG: A Topic-Guided Wikipedia Abstract Generator
Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques.
