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Temporal-LLMs

Materials for paper "Are large language models temporally grounded?" (pdf)

Screentshot

Environment Setup

Configure your conda environment from our provided llama-hf_environment.yml by,

conda env create -f llama-hf_environment.yml
conda activate llama-hf

Prerequisite

GPT

Our experiments require the inference with these following models,

  1. davinci,
  2. text-davinci-002,
  3. text-davinci-003.

You will need to prepare your own OpenAI API call and access for these models in advance.

LLaMA Chceckpoints

Our experiments require the inference with these following models,

  1. LLaMA-7B
  2. LLaMA-13B
  3. LLaMA-33B
  4. LLaMA-65B
  5. LLaMA-2-7B
  6. LLaMA-2-13B
  7. LLaMA-2-70B
  8. LLaMA-2-7B-chat
  9. LLaMA-2-13B-chat
  10. LLaMA-2-70B-chat

We recommend you to download all files from huggingface hub in to a local path, see the LLaMA here and here for LLaMA-2.

Usage

McTACO Experiment

Inference for GPT & LLaMA

Using the following scripts for testing GPT and LLaMA with zero/few-shot prompting,

sh run-mctaco-gpt.sh
sh run-mctaco-llama.sh

Evaluation

We provide our evaluation script based on the original McTACO's evaluation. We recommend you to get familiar with its original repository as the first step,

sh eval-mctaco.sh

Run CaTeRS Experiment

Inference for GPT & LLaMA

Using the following scripts for testing GPT and LLaMA models with few-shot prompting,

sh run-caters-gpt.sh
sh run-caters-llama.sh

Evaluation

Our evaluation script is strictly following the evaluation of temporal-bart model. Again, we recommend you to get familiar with its repository as well,

To run the evaluation, simply run this code,

python3 eval-caters.py $OUTPUT_PATH $MODEL_NAME

Taking Llama-2-70b-chat-hf as an example,

python3 eval-caters.py llama-output/caters/caters-fs-pt1-output-icl3/ Llama-2-70b-chat-hf

Run TempEval-QA-bi Experiment

Inference for GPT & LLaMA

You can use the following scripts for inference with GPT,

sh run-tempeval-gpt.sh

You can use the following scripts for doing zero/few-shot + likelihood/decoding-based evaluation, and chain-of-thought experiments for LLaMA models.

sh run-tempeval-llama.sh

Evaluation for Bi-directional Reasoning

To run the bi-directional evaluation in checking model's reasoning consistency, simply run this code,

python3 eval-tempeval-bi.py $OUTPUT_PATH $MODEL_NAME

Taking Llama-2-70b-chat-hf as an example,

python3 eval-tempeval-bi.py llama-output/tempeval-qa-bi/fs-bi-pt1-icl3-output-likelihood/ Llama-2-70b-chat-hf

Evaluation for Chain-of-thought Reasoning

To evaluate the reasoning performance for LLaMA with the chain-of-thought prompting, simply run this code,

python3 eval-tempeval-bi-cot.py $OUTPUT_PATH $MODEL_NAME

Taking Llama-2-70b-chat-hf as an example,

python3 eval-tempeval-bi-cot.py llama-output/tempeval-qa-bi/fs-bi-pt1-icl3-cot-output-likelihood/ Llama-2-70b-chat-hf

Model Outputs

We provide all our model's outputs in all datasets in gpt-output and llama-output for reproducing the results reported in our paper.

Citation

@misc{qiu2023large,
      title={Are Large Language Models Temporally Grounded?}, 
      author={Yifu Qiu and Zheng Zhao and Yftah Ziser and Anna Korhonen and Edoardo M. Ponti and Shay B. Cohen},
      year={2023},
      eprint={2311.08398},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}