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Found 9 papers, 6 papers with code
Can Models Help Us Create Better Models? Evaluating LLMs as Data Scientists
We present a benchmark for large language models designed to tackle one of the most knowledge-intensive tasks in data science: writing feature engineering code, which requires domain knowledge in addition to a deep understanding of the underlying problem and data structure.
Arctic-TILT. Business Document Understanding at Sub-Billion Scale
The vast portion of workloads employing LLMs involves answering questions grounded on PDF or scan content.
Document Understanding Dataset and Evaluation (DUDE)
We call on the Document AI (DocAI) community to reevaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks.
STable: Table Generation Framework for Encoder-Decoder Models
The output structure of database-like tables, consisting of values structured in horizontal rows and vertical columns identifiable by name, can cover a wide range of NLP tasks.
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer
We address the challenging problem of Natural Language Comprehension beyond plain-text documents by introducing the TILT neural network architecture which simultaneously learns layout information, visual features, and textual semantics.
Ranked #7 on
Visual Question Answering (VQA)
on InfographicVQA
(using extra training data)
From Dataset Recycling to Multi-Property Extraction and Beyond
This paper investigates various Transformer architectures on the WikiReading Information Extraction and Machine Reading Comprehension dataset.
Successive Halving Top-k Operator
We propose a differentiable successive halving method of relaxing the top-k operator, rendering gradient-based optimization possible.
Sparsifying Transformer Models with Trainable Representation Pooling
A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust trainable top-$k$ operator.
Ranked #2 on
Text Summarization
on arXiv Summarization Dataset
On the Multi-Property Extraction and Beyond
In this paper, we investigate the Dual-source Transformer architecture on the WikiReading information extraction and machine reading comprehension dataset.
