TG-LLM

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[ACL 24 main] Large Language Models Can Learn Temporal Reasoning

TG-LLM 최신버젼 다운로드

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## TG-LLM: Large Language Models Can Learn Temporal Reasoning

This repository contains the code for the paper [ACL 24 (main)] [Large Language Models Can Learn Temporal Reasoning](https://arxiv.org/pdf/2401.06853.pdf).

Our framework (TG-LLM) performs **temporal reasoning** in two steps: 1) **Text-to-Temporal Graph translation:** generate (relevant) temporal graph given the context and keyword (extracted from questions); 2) **Temporal Graph Reasoning:** perform deliberate Chain-of-Thought reasoning over the temporal graph.

<br>

We use **contrastive-learning-score-based CoT bootstrapping** (left) and **graph data augmentation** (right) to further improve the reasoning-over-graph performance.

<br>

## Quick Start

We use [Hugging Face](https://huggingface.co/) platform to load the Llama2 model family. Make sure you have an account ([Guidance](https://huggingface.co/blog/llama2)).

The structure of the file folder should be like
```sh
TG-LLM/
│
├── materials/
│
├── model_weights/
│
├── results/
│
└── src/
```

<h4> Preparation: </h4>

```sh
# git clone this repo

# create a new environment with anaconda and install the necessary Python packages

# install hugging face packages to load Llama2 models and datasets

# create the folders
cd TG-LLM
mkdir model_weights
mkdir results
cd src
```

## Download pre-tuned models
We provide model checkpoints for the supervised fine-tuned [models](https://huggingface.co/sxiong/TG-LLM) ([Configuration] base model: Llama2-13b-chat-hf, use_LoRA: Ture, prompt format: plain, use_TG: True, CoT_bs: True, data_aug: True).

## Datasets

All the datasets (TGQA, TimeQA, TempReason) can be found [here](https://huggingface.co/datasets/sxiong/TGQA).

To download the dataset, install [Huggingface Datasets](https://huggingface.co/docs/datasets/quickstart) and then use the following command:

```python
from datasets import load_dataset
dataset = load_dataset("sxiong/TGQA", "TGQA_Story_TG_Trans") # Six configs available: "TGQA_Story_TG_Trans", "TGQA_TGR", "TempReason_Story_TG_Trans", "TempReason_TGR", "TimeQA_Story_TG_Trans", "TimeQA_TGR"
print(dataset) # Print dataset to see the statistics and available splits
split = dataset['train']  # Multiple splits available: "train", "val", "test"
```

<h4> For our TG-LLM framework: </h4>

- Step 1: text-to-temporal graph translation

```sh
# Train and test on TGQA dataset
accelerate launch SFT_text_to_TG_Trans.py --dataset TGQA --train --print_prompt
accelerate launch SFT_text_to_TG_Trans.py --dataset TGQA --test --ICL --print_prompt

# Train and test on TimeQA dataset (Since some stories in TimeQA and TempReason are too long to feed into Llama2 (max_context_len: 4096), it is recommended to shorten the story.)
accelerate launch SFT_text_to_TG_Trans.py --dataset TimeQA --train --print_prompt --shorten_story
accelerate launch SFT_text_to_TG_Trans.py --dataset TimeQA --test --ICL --print_prompt --shorten_story

# Train on TGQA, test on TimeQA
accelerate launch SFT_text_to_TG_Trans.py --dataset TGQA --train --transferred_dataset TimeQA --print_prompt
accelerate launch SFT_text_to_TG_Trans.py --dataset TimeQA --test --shorten_story --ICL --print_prompt --transferred
```

- Step 2: temporal graph reasoning

```sh
# Obtain CoT sampling prob
accelerate launch CoT_bootstrap.py --dataset TGQA --print_prompt

# Train and test on TGQA dataset
accelerate launch SFT_TG_Reasoning.py --dataset TGQA --train --CoT_bs --data_aug --print_prompt
accelerate launch SFT_TG_Reasoning.py --dataset TGQA --test --ICL --print_prompt

# To obtain inference results based on perplexity
accelerate launch SFT_TG_Reasoning_ppl.py --dataset TGQA --ICL --print_prompt
```

<h4> For evaluation: </h4>

```sh
# To evaluate our framework
python Evaluation.py --dataset TGQA --model Llama2-13b --SFT
```

## Prompt Format
The original format used in the paper is plain text. We also provide the option of **JSON** which is much easier to parse and doesn't hurt the performance much. Please use the command **--prompt_format** to change the format seamlessly.

## Accelerate with Multi GPUs
The default training/inference arguments are for a single A100 (GPU memory: 80G). If you have multiple GPUs, the **training** process can be accelerated in a distributed way. Here we recommend the library of **DeepSpeed** [[docs]](https://huggingface.co/docs/peft/en/accelerate/deepspeed).

Also, you can accelerate the **inference** with multiple GPUs [[src/Example_accelerate_inference.py]](https://raw.githubusercontent.com/xiongsiheng/TG-LLM/main/src/Example_accelerate_inference.py).

## Comparison

<h4> For other leading LLMs (GPT series/Llama2 family): </h4>

- Use in-context learning only

```sh
# Test on TGQA with Llama2-13b with ICL only
python Inference_in_context_learning.py --dataset TGQA --model Llama2-13b --CoT --ICL --print_prompt

# To obtain inference results based on perplexity
python Inference_in_context_learning_ppl.py --dataset TGQA --model Llama2-13b --CoT --ICL --print_prompt
```

- Use SFT with vanilla CoT (story, question, CoT, answer)
```sh
# Train and test on TGQA dataset
accelerate launch SFT_TG_Reasoning.py --dataset TGQA --train --print_prompt --no_TG
accelerate launch SFT_TG_Reasoning.py --dataset TGQA --test --ICL --print_prompt --no_TG

# To obtain inference results based on perplexity
accelerate launch SFT_TG_Reasoning_ppl.py --dataset TGQA --ICL --print_prompt --no_TG
```

<h4> For evaluation: </h4>

```sh
# To evaluate other leading LLMs with ICL only
python Evaluation.py --dataset TGQA --model Llama2-13b --ICL_only --CoT

# To evaluate other leading LLMs with SFT on vanilla CoT
python Evaluation.py --dataset TGQA --model Llama2-13b --SFT --no_TG
```

## Contact
If you have any inquiries, please feel free to raise an issue or reach out to sxiong45@gatech.edu.

## Citation
```
@inproceedings{xiong2024large,
  title={Large language models can learn temporal reasoning},
  author={Xiong, Siheng and Payani, Ali and Kompella, Ramana and Fekri, Faramarz},
  booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={10452--10470},
  year={2024}
}
```

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