Model Card for TG-LLM

TG-LLM consists of supervised fine-tuned models designed for temporal reasoning with large language models (LLMs). It includes two primary tasks:

1. Story-to-Temporal-Graph Translation (story_TG_trans) – converting a narrative into its corresponding temporal graph.
2. Temporal-Graph Reasoning (TGR) – reasoning over a given temporal graph to answer questions.

Model Details

TGQA_story_TG_trans

• Base Model: meta-llama/Llama-2-13b-chat-hf

• LoRA Configuration:

  • lora_alpha: 8
  • r: 8
  • target_modules: ["q_proj", "k_proj", "o_proj", "v_proj"]
  • bias: "none"

TGQA_TGR

• Base Model: meta-llama/Llama-2-13b-chat-hf

• LoRA Configuration:

  • lora_alpha: 8
  • r: 8
  • target_modules: ["q_proj", "k_proj", "o_proj", "v_proj"]
  • bias: "none"

For more details, please visit the TG-LLM GitHub repository.

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}
}