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---
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license: cc-by-4.0
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language:
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- en
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configs:
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- config_name: Temporal
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data_files:
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- split: default
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path: Temporal/*.json
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- config_name: Invariant
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data_files:
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- split: default
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path: Invariant/*.json
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task_categories:
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- question-answering
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- text-generation
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tags:
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- circuit
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- temporal
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- knowledge
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- triplet
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size_categories:
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- 10K<n<100K
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---
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# \[ACL 2025\] Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information |
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<center><img src = "https://cdn-uploads.huggingface.co/production/uploads/5efbdc4ac3896117eab961a9/yzXDiuGZUHCaVkERZFSIO.png" width="1000" height="1000"></center> |
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**This repository contains two separate subsets of data (configs):** |
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- **Temporal**: JSON files in `Temporal` that include temporal knowledge. |
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- **Invariant**: JSON files in `Invariant` that describe time-invariant knowledge based on [LRE](https://arxiv.org/abs/2308.09124). |
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Each subset has its own schema. By defining them as two configs in the YAML header above, Hugging Face’s Dataset Viewer will show **“Temporal”** and **“Invariant”** as separate options in the configuration dropdown, allowing you to explore each schema independently without a schema‐mismatch error. |
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--- |
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## Dataset Overview |
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**Motivation:** |
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Large language models (LLMs) often struggle to answer questions whose answers change over time. We investigated whether there exist specialized attention heads—**Temporal Heads**—that are triggered by explicit dates (e.g., “In 2004, …”) or by implicit textual cues (e.g., “In the year …”) and that help the model recall or update time-specific facts. |
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- **Method:** Using [Knowledge Circuit](https://arxiv.org/abs/2405.17969) analysis, we identified attention heads in LLMs that strongly activate on temporal signals (timestamps, years, etc.). |
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- **Findings:** These Temporal Heads are crucial for time-sensitive recall. When you ablate (disable) them, the model’s performance on time-dependent questions degrades significantly, whereas its performance on static (time-invariant) knowledge remains almost unchanged. |
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- **Implications:** By manipulating the outputs of these specific heads, one can potentially edit or correct a model’s temporal knowledge directly (e.g., if its internal knowledge about “Who was president in 1999?” is outdated). |
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--- |
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## Usage |
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```python |
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from datasets import load_dataset |
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# 1. Load the "Temporal" config |
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# - Each example in this split has fields like: |
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# { "name": ..., "prompt_templates": [...], "samples": [ { "subject": ..., "object": ..., "time": ... }, ... ], ... } |
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Temporal = load_dataset("dmis-lab/TemporalHead", "Temporal")["default"] |
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# 2. Load the "Invariant" config |
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# - Each example here has fields like: |
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# { "name": ..., "prompt_templates": [...], "properties": { "relation_type": ..., ... }, "samples": [ { "subject": ..., "object": ... }, ... ], ... } |
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Invariant = load_dataset("dmis-lab/TemporalHead", "Invariant")["default"] |
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``` |
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--- |
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## Citation and Acknowledgements |
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If you find our work is useful in your research, please consider citing our [paper](https://arxiv.org/abs/2502.14258): |
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``` |
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@article{park2025does, |
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title={Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information}, |
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author={Park, Yein and Yoon, Chanwoong and Park, Jungwoo and Jeong, Minbyul and Kang, Jaewoo}, |
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journal={arXiv preprint arXiv:2502.14258}, |
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year={2025} |
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} |
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``` |
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We also gratefully acknowledge the following open-source repositories and kindly ask that you cite their accompanying papers as well. |
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[1] https://github.com/zjunlp/KnowledgeCircuits |
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[2] https://github.com/hannamw/eap-ig |
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[3] https://github.com/evandez/relations |
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--- |
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## Contact |
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For any questions or issues, feel free to reach out to [522yein (at) korea.ac.kr]. |
