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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[EMNLP 2022] Unifying and multi-tasking structured knowledge grounding with language models
| Date | Stars |
|---|---|
| 2026-07-31 | 566 |
| 2026-08-06 | 566 |
Today
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This week
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Momentum
0.0
growth rate 0.00%/day
# UnifiedSKG:books:: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
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<a href="https://img.shields.io/badge/PRs-Welcome-red">
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<a href="https://img.shields.io/github/last-commit/HKUNLP/UnifiedSKG?color=green">
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<a href="https://colab.research.google.com/drive/1f9yTXC3GpSyRJOjzsKceG_bhk-Cw71Ga#scrollTo=r_3-DN0SvC97">
<img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg">
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Code for EMNLP 2022 (oral) paper [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://arxiv.org/abs/2201.05966). Please refer to our [project page](https://unifiedskg.com/) for up-to-date related resources (e.g., papers, code, tools, tutorials) for Structured Knowledge Grounding. Load our checkpoints from [HuggingFace Model Hub](https://huggingface.co/hkunlp).
<img src="pics/unifiedskg.png" align="middle" width="100%">
**S**tructured **k**nowledge **g**rounding (**SKG**) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases. Since the inputs and outputs of SKG tasks are heterogeneous, they were historically studied in separate by different communities, which limits systematic and compatible research on SKG. In this paper, we overcome this limitation by proposing the **UnifiedSKG framework**, which unifies **21 SKG tasks** into the text-to-text format, aiming to promote systematic SKG research, instead of being exclusive to a single task, domain, or dataset. We show that large language models like T5, with simple modification when necessary, achieve **state-of-the-art performance on nearly all 21 tasks**. UnifiedSKG facilitates **multi-task learning**. We show that multi-task prefix-tuning benefits most tasks, largely improving the overall performance. UnifiedSKG is a challenging testbed for **zero-shot and few-shot learning**, which T0, GPT-3, and Codex struggle in. UnifiedSKG also enables a series of controlled experiments on **structured knowledge encoding** variants across SKG tasks. We find that T5’s sensitivity to structured knowledge encoding variations varies across tasks.
**UnifiedSKG** is easily extensible. Your **pull requests** to add datasets, settings, metrics, models, and new features to UnifiedSKG are highly welcome!
## Updates
- **2022-03-12**: Check out the seq2seq data we processed for you [here](https://drive.google.com/drive/folders/1GXigUv3MU-Sh4XiY6Wz3xVeNT_s0SuON) by UnifiedSKG if you want to make your own attempts instead of using the huggingface loaders in our framework.
- **2022-01-12**: We released our [code](https://github.com/HKUNLP/UnifiedSKG), [colab demo](https://colab.research.google.com/drive/1f9yTXC3GpSyRJOjzsKceG_bhk-Cw71Ga#scrollTo=r_3-DN0SvC97), [weights](https://huggingface.co/hkunlp) and [project page](https://unifiedskg.com). Check it out!
## Content
- [UnifiedSKG: Unifying and Multi-Tasking **S**tructured **K**nowledge **G**rounding with Text-to-Text Language Models](#unifiedskgbooks-unifying-and-multi-tasking-structured-knowledge-grounding-with-text-to-text-language-models)
* [Cloning this Repo](#cloning-this-repo)
* [Dependencies](#dependencies)
* [Usage](#usage)
+ [Environment setup](#environment-setup)
+ [Wandb setup](#wandb-setup)
+ [Training](#training)
+ [Load weights](#load-weights)
* Introduction of each directory
+ [configure](https://github.com/HKUNLP/UnifiedSKG/tree/master/configure)
+ [metrics](https://github.com/HKUNLP/UnifiedSKG/tree/master/metrics)
+ [models](https://github.com/HKUNLP/UnifiedSKG/tree/master/models)
+ [seq2seq_construction](https://github.com/HKUNLP/UnifiedSKG/tree/master/seq2seq_constrExcerpt of 19,297 characters
Read on GitHub
Tianbao Xie · The University of Hong Kong · Hong Kong
37
37
12
Tao Yu · NLPer@XLANG Lab
6
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e3d1c5d6796102e3, topic:nlp, topic:natural-language-processing
matched fp:e3d1c5d6796102e3, topic:pytorch
matched fp:e3d1c5d6796102e3, topic:question-answering