No confident code implementation found for this paper. 1 possible match can be shown below.
This paper presents an opinion on the potential of using large language models to query on both unstructured and structured data. It also outlines some research challenges related to the topic of building question-answering systems for both types of data.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
Bridging the Gap: Deciphering Tabular Data Using Large Language Model
Reimagining Retrieval Augmented Language Models for Answering Queries
StructGPT: A General Framework for Large Language Model to Reason over Structured Data
Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries
BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer
Data Distribution Bottlenecks in Grounding Language Models to Knowledge Bases
@article{tan2023unstructured,
title = {Unstructured and structured data: Can we have the best of both worlds with large language models?},
author = {Wang-Chiew Tan},
year = {2023},
eprint = {2304.13010},
archivePrefix = {arXiv},
url = {http://arxiv.org/abs/2304.13010v2}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/2304.13010)