Search Results for author:
Found 12 papers, 5 papers with code
Distilling Mathematical Reasoning Capabilities into Small Language Models
This work addresses the challenge of democratizing advanced Large Language Models (LLMs) by compressing their mathematical reasoning capabilities into sub-billion parameter Small Language Models (SLMs) without compromising performance.
Unifying Structured Data as Graph for Data-to-Text Pre-Training
In this paper, we unify different types of structured data (i. e., table, key-value data, knowledge graph) into the graph format and cast different data-to-text generation tasks as graph-to-text generation.
Separate and Locate: Rethink the Text in Text-based Visual Question Answering
To tackle these problems, we propose a novel method named Separate and Locate (SaL) that explores text contextual cues and designs spatial position embedding to construct spatial relations between OCR texts.
A Survey on Model Compression for Large Language Models
As these challenges become increasingly pertinent, the field of model compression has emerged as a pivotal research area to alleviate these limitations.
CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality
To alleviate these limitations, in this paper, we present CATS, a pragmatic Chinese answer-to-sequence dataset with large scale and high quality.
Plan-then-Seam: Towards Efficient Table-to-Text Generation
Table-to-text generation aims at automatically generating text to help people conveniently obtain salient information in tables.
Graph-to-Text Generation with Dynamic Structure Pruning
Most graph-to-text works are built on the encoder-decoder framework with cross-attention mechanism.
Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering
Texts in scene images convey critical information for scene understanding and reasoning.
Optical Character Recognition
Optical Character Recognition (OCR)
+3
Improving Encoder by Auxiliary Supervision Tasks for Table-to-Text Generation
However, it is hard for a vanilla encoder to capture these.
Ranked #1 on
Table-to-Text Generation
on RotoWire
Learning Better Representation for Tables by Self-Supervised Tasks
Secondly, the target texts in training dataset may contain redundant information or facts do not exist in the input tables.
Two-Level Residual Distillation based Triple Network for Incremental Object Detection
Modern object detection methods based on convolutional neural network suffer from severe catastrophic forgetting in learning new classes without original data.
Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning
As a proxy task, it converts rich self-supervised representations into video clip operations (options), which enhances the flexibility and reduces the complexity of representation learning.
Ranked #11 on
Self-supervised Video Retrieval
on HMDB51
