---
license: mit
language:
- en
task_categories:
- text-generation
- question-answering
tags:
- temporal-reasoning
- temporal-graph
- llama-2
- peft
- lora
configs:
- config_name: TGQA_story_TG_trans
  data_files:
  - split: test
    path: TGQA_story_TG_trans.jsonl
- config_name: TGQA_TGR
  data_files:
  - split: test
    path: TGQA_TGR.jsonl
---

# TG-LLM Output

This dataset contains inference outputs from the two-stage **TG-LLM** framework
on the TGQA test set. TG-LLM performs temporal reasoning in two steps:

1. **Story-to-Temporal-Graph Translation (Story2TG):** converts a story into a
   temporal graph.
2. **Temporal-Graph Reasoning (TGR):** reasons over the predicted temporal graph
   to answer temporal questions.

The original TGQA dataset is available at
[`sxiong/TGQA`](https://huggingface.co/datasets/sxiong/TGQA), and the source code
is available in the
[`TG-LLM`](https://github.com/xiongsiheng/TG-LLM) repository.

## Models

Both LoRA adapters use
[`meta-llama/Llama-2-13b-chat-hf`](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)
as the base model.

- Story2TG:
  [`sxiong/TG-LLM-TGQA-Story2TG-Llama-2-13B-LoRA`](https://huggingface.co/sxiong/TG-LLM-TGQA-Story2TG-Llama-2-13B-LoRA)
- TGR:
  [`sxiong/TG-LLM-TGQA-TGR-Llama-2-13B-LoRA`](https://huggingface.co/sxiong/TG-LLM-TGQA-TGR-Llama-2-13B-LoRA)

## Dataset Configurations

| Configuration | Split | Rows | Description |
| --- | --- | ---: | --- |
| `TGQA_story_TG_trans` | `test` | 101 | Stories, reference temporal graphs, prompts, and predicted temporal graphs. |
| `TGQA_TGR` | `test` | 3,316 | Temporal questions and answers generated from the Story2TG predictions. |

The TGR prompts use the **predicted** temporal graphs produced in the first
stage. The `meta.TG` field retains the reference temporal graph from TGQA for
analysis; it is not the graph included in the TGR prompt.

## Inference Configuration

- Precision: BF16
- Prompt format: plain text
- In-context learning: disabled
- Decoding: deterministic greedy decoding (`do_sample=False`, `num_beams=1`)
- Story2TG maximum new tokens: 1,024
- TGR maximum new tokens: 512

No sample reached its maximum generation length.

## Data Fields

### `TGQA_story_TG_trans`

- `id`: Story identifier.
- `story`: Input story.
- `TG`: Reference temporal graph from TGQA.
- `prompt`: Full prompt provided to the Story2TG model.
- `prediction`: Temporal graph generated by the Story2TG model.

### `TGQA_TGR`

- `id`: Question identifier.
- `story`: Source story.
- `question`: Temporal reasoning question.
- `answer`: List of reference answers.
- `prompt`: Full TGR prompt containing the predicted temporal graph.
- `prediction`: Generated reasoning trace and answer.
- `correct`: Whether the parsed prediction exactly matches a reference answer.
- `meta`: Additional reference data:
  - `TG`: Reference temporal graph from TGQA.
  - `candidates`: Candidate answers.
  - `external knowledge`: Temporal relations and arithmetic supplied in the
    prompt.
  - `Q-Type`: TGQA question-type index.

For `correct`, answers are compared case-insensitively after removing spaces.
For question types 2 and 3, only the leading numeric value is compared, matching
the TG-LLM evaluation protocol.

## Results

All 3,316 TGR predictions were successfully parsed: Exact match (macro average) -- 77.5%.

### Accuracy by Task

| Q-Type | Task | Correct / Total | Exact Match |
| ---: | --- | ---: | ---: |
| 0 | Pairwise start-time ordering | 290 / 303 | 95.7% |
| 1 | Five-event chronological ordering | 830 / 990 | 83.8% |
| 2 | Event duration | 168 / 221 | 76.0% |
| 3 | Time between event starts | 238 / 292 | 81.5% |
| 4 | Event start time | 280 / 303 | 92.4% |
| 5 | Same start year | 289 / 303 | 95.4% |
| 6 | Event overlap / still happening | 147 / 212 | 69.3% |
| 7 | Immediate predecessor / successor | 210 / 504 | 41.7% |
| 8 | Event-duration comparison | 116 / 188 | 61.7% |

## Loading the Data

After uploading this directory to a Hugging Face dataset repository, load either
configuration with `datasets`:

```python
from datasets import load_dataset

story_to_tg = load_dataset(
    "sxiong/TG_LLM_output",
    "TGQA_story_TG_trans",
    split="test",
)

tgr = load_dataset(
    "sxiong/TG_LLM_output",
    "TGQA_TGR",
    split="test",
)
```

## Citation

```bibtex
@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}
}
```