metadata

license: mit
datasets:
  - TIGER-Lab/SKGInstruct
language:
  - en

🏗️ StructLM: Towards Building Generalist Models for Structured Knowledge Grounding

Project Page: https://tiger-ai-lab.github.io/StructLM/

Paper: Arxiv link not yet announced

Code: https://github.com/TIGER-AI-Lab/StructLM

Introduction

StructLM, is a series of open-source large language models (LLMs) finetuned for structured knowledge grounding (SKG) tasks. We release 3 models:

7B | StructLM-7B

13B | StructLM-13B

34B | StructLM-34B

Training Data

These models are trained on 🤗 SKGInstruct Dataset, an instruction-tuning dataset containing mixture of 19 SKG tasks combined with 🤗 SlimOrca. Check out the dataset card for more details.

Training Procedure

The models are fine-tuned with CodeLlama-Instruct-hf models as base models. Each model is trained for 3 epochs, and the best checkpoint is selected.

Evaluation

Here are a subset of model evaluation results:

Held in

Model	ToTTo	GrailQA	CompWebQ	MMQA	Feverous	Spider	TabFact	Dart
StructLM-7B	49.4	80.4	78.3	85.2	84.4	72.4	80.8	62.2
StructLM-13B	49.3	79.2	80.4	86.0	85.0	74.1	84.7	61.4
StructLM-34B	50.2	82.2	81.9	88.1	85.7	74.6	86.6	61.8

Held out

Model	BIRD	InfoTabs	FinQA	SQA
StructLM-7B	22.3	55.3	27.3	49.7
StructLM-13B	22.8	58.1	25.6	36.1
StructLM-34B	24.7	61.8	36.2	44.2

Usage

You can use the models through Huggingface's Transformers library. Check our Github repo for the evaluation code: https://github.com/TIGER-AI-Lab/StructLM

Prompt Format

***IMPORTANT***

For this 34B model, the prompt format (different from 7B) is

[INST] [INST] <<SYS>>
You are an AI assistant that specializes in analyzing and reasoning
over structured information. You will be given a task, optionally
with some structured knowledge input. Your answer must strictly
adhere to the output format, if specified.
<</SYS>>
{instruction} [/INST] [/INST]

To linearize structured input of various types during training, we follow the linearization procedures from UnifiedSKG, so using this format during prompting will be most effective. To see concrete examples of this linearization, you can directly reference the 🤗 SKGInstruct Dataset.

Intended Uses

These models are trained for research purposes. They are designed to be proficient in interpreting linearized structured input. Downstream uses can potentially include various applications requiring the interpretation of structured data.

Limitations

While we've tried to build an SKG-specialized model capable of generalizing, we have shown that this is a challenging domain, and it may lack performance characteristics that allow it to be directly used in chat or other applications.

Citation

If you use the models, data, or code from this project, please cite the original paper:

to be updated