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TISER

Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models

This repository contains the data for the paper (ACL 2025 Main): Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models.

TISER incorporates a multi-stage inference pipeline that combines explicit reasoning, timeline construction, and iterative self-reflection. The key idea behind our approach is to empower LLMs to adapt by scaling their internal reasoning process during inference. TISER enables models to systematically organize temporal information, verify their inferences, and refine their outputs.

Train Data Format

Each entry in the TISER train dataset is a JSON object containing six fields: dataset_name, question_id, question, answer, prompt, and output. The question field specifies the temporal question being asked, while answer contains the expected short-form response (e.g., an entity or number). The prompt provides detailed instructions for a Chain of Thought (CoT) reasoning process with reflection, guiding the model to reason step-by-step, extract temporal events, reflect on its logic, and produce a final answer. The output field contains the full model-generated response adhering to this reasoning format. This structure supports supervised training of models to perform temporal reasoning and answer generation.

Test Data Format

Each test example in the TISER test dataset is represented as a single JSON object containing five fields: dataset_name, which specifies the split or task; question_id, a unique identifier for the query; question, the temporal reasoning prompt itself; prompt, which embeds the full Chain-of-Thought template (including <reasoning>, <timeline>, <reflection> and <answer> tags) that the model should follow when generating its response; and answer, the held-out ground-truth output against which the model’s <answer> section is evaluated. Unlike the training format, there is no output field in test records, since models read the prompt and produce only an <answer> that is scored directly against the provided answer key.

Data Subsets Provenance

Original data subsets before our preprocessing were extracted from the following HuggingFace URLs

Citation

@misc{bazaga2025learningreasontimetimeline,
title={Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models}, author={Adrián Bazaga and Rexhina Blloshmi and Bill Byrne and Adrià de Gispert},
year={2025},
eprint={2504.05258},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2504.05258}, }

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.

Contact

For feedback or questions please contact Adrián Bazaga

About

[ACL 2025] Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models

arxiv.org/abs/2504.05258

Topics

language-modellarge-language-modelsllms-reasoningreasoningtemporal-datasettemporal-reasoning

Resources

Readme
MIT-0 license

Code of conduct

Code of conduct

Contributing

Contributing

Security policy

Security policy
Activity
Custom properties

Stars

14 stars

Watchers

0 watching

Forks

5 forks
Report repository

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Generated from amazon-archives/__template_MIT-0
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Repository files navigation
TISER
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
This repository contains the data for the paper (ACL 2025 Main): Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models.
TISER incorporates a multi-stage inference pipeline that combines explicit reasoning, timeline construction, and iterative self-reflection. The key idea behind our approach is to empower LLMs to adapt by scaling their internal reasoning process during inference. TISER enables models to systematically organize temporal information, verify their inferences, and refine their outputs.
Train Data Format
Each entry in the TISER train dataset is a JSON object containing six fields: dataset_name, question_id, question, answer, prompt, and output. The question field specifies the temporal question being asked, while answer contains the expected short-form response (e.g., an entity or number). The prompt provides detailed instructions for a Chain of Thought (CoT) reasoning process with reflection, guiding the model to reason step-by-step, extract temporal events, reflect on its logic, and produce a final answer. The output field contains the full model-generated response adhering to this reasoning format. This structure supports supervised training of models to perform temporal reasoning and answer generation.
Test Data Format
Each test example in the TISER test dataset is represented as a single JSON object containing five fields: dataset_name, which specifies the split or task; question_id, a unique identifier for the query; question, the temporal reasoning prompt itself; prompt, which embeds the full Chain-of-Thought template (including <reasoning>, <timeline>, <reflection> and <answer> tags) that the model should follow when generating its response; and answer, the held-out ground-truth output against which the model’s <answer> section is evaluated. Unlike the training format, there is no output field in test records, since models read the prompt and produce only an <answer> that is scored directly against the provided answer key.
Data Subsets Provenance
Original data subsets before our preprocessing were extracted from the following HuggingFace URLs
Citation
@misc{bazaga2025learningreasontimetimeline,
title={Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models}, author={Adrián Bazaga and Rexhina Blloshmi and Bill Byrne and Adrià de Gispert},
year={2025},
eprint={2504.05258},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2504.05258}, }

Security
See CONTRIBUTING for more information.
License
This library is licensed under the MIT-0 License. See the LICENSE file.
Contact
For feedback or questions please contact Adrián Bazaga
About
[ACL 2025] Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
arxiv.org/abs/2504.05258
Topics
language-modellarge-language-modelsllms-reasoningreasoningtemporal-datasettemporal-reasoning
Resources
Readme
MIT-0 license
Code of conduct
Code of conduct
Contributing
Contributing
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Repository files navigation
TISER
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
This repository contains the data for the paper (ACL 2025 Main): Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models.
TISER incorporates a multi-stage inference pipeline that combines explicit reasoning, timeline construction, and iterative self-reflection. The key idea behind our approach is to empower LLMs to adapt by scaling their internal reasoning process during inference. TISER enables models to systematically organize temporal information, verify their inferences, and refine their outputs.
Train Data Format
Each entry in the TISER train dataset is a JSON object containing six fields: dataset_name, question_id, question, answer, prompt, and output. The question field specifies the temporal question being asked, while answer contains the expected short-form response (e.g., an entity or number). The prompt provides detailed instructions for a Chain of Thought (CoT) reasoning process with reflection, guiding the model to reason step-by-step, extract temporal events, reflect on its logic, and produce a final answer. The output field contains the full model-generated response adhering to this reasoning format. This structure supports supervised training of models to perform temporal reasoning and answer generation.
Test Data Format
Each test example in the TISER test dataset is represented as a single JSON object containing five fields: dataset_name, which specifies the split or task; question_id, a unique identifier for the query; question, the temporal reasoning prompt itself; prompt, which embeds the full Chain-of-Thought template (including <reasoning>, <timeline>, <reflection> and <answer> tags) that the model should follow when generating its response; and answer, the held-out ground-truth output against which the model’s <answer> section is evaluated. Unlike the training format, there is no output field in test records, since models read the prompt and produce only an <answer> that is scored directly against the provided answer key.
Data Subsets Provenance
Original data subsets before our preprocessing were extracted from the following HuggingFace URLs
Citation
@misc{bazaga2025learningreasontimetimeline,
title={Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models}, author={Adrián Bazaga and Rexhina Blloshmi and Bill Byrne and Adrià de Gispert},
year={2025},
eprint={2504.05258},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2504.05258}, }

Security
See CONTRIBUTING for more information.
License
This library is licensed under the MIT-0 License. See the LICENSE file.
Contact
For feedback or questions please contact Adrián Bazaga
About
[ACL 2025] Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
arxiv.org/abs/2504.05258
Topics
language-modellarge-language-modelsllms-reasoningreasoningtemporal-datasettemporal-reasoning
Resources
Readme
MIT-0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
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Generated from amazon-archives/__template_MIT-0

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
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Repository files navigation
TISER
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
This repository contains the data for the paper (ACL 2025 Main): Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models.
TISER incorporates a multi-stage inference pipeline that combines explicit reasoning, timeline construction, and iterative self-reflection. The key idea behind our approach is to empower LLMs to adapt by scaling their internal reasoning process during inference. TISER enables models to systematically organize temporal information, verify their inferences, and refine their outputs.
Train Data Format
Each entry in the TISER train dataset is a JSON object containing six fields: dataset_name, question_id, question, answer, prompt, and output. The question field specifies the temporal question being asked, while answer contains the expected short-form response (e.g., an entity or number). The prompt provides detailed instructions for a Chain of Thought (CoT) reasoning process with reflection, guiding the model to reason step-by-step, extract temporal events, reflect on its logic, and produce a final answer. The output field contains the full model-generated response adhering to this reasoning format. This structure supports supervised training of models to perform temporal reasoning and answer generation.
Test Data Format
Each test example in the TISER test dataset is represented as a single JSON object containing five fields: dataset_name, which specifies the split or task; question_id, a unique identifier for the query; question, the temporal reasoning prompt itself; prompt, which embeds the full Chain-of-Thought template (including <reasoning>, <timeline>, <reflection> and <answer> tags) that the model should follow when generating its response; and answer, the held-out ground-truth output against which the model’s <answer> section is evaluated. Unlike the training format, there is no output field in test records, since models read the prompt and produce only an <answer> that is scored directly against the provided answer key.
Data Subsets Provenance
Original data subsets before our preprocessing were extracted from the following HuggingFace URLs
Citation
@misc{bazaga2025learningreasontimetimeline,
title={Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models}, author={Adrián Bazaga and Rexhina Blloshmi and Bill Byrne and Adrià de Gispert},
year={2025},
eprint={2504.05258},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2504.05258}, }

Security
See CONTRIBUTING for more information.
License
This library is licensed under the MIT-0 License. See the LICENSE file.
Contact
For feedback or questions please contact Adrián Bazaga
About
[ACL 2025] Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
arxiv.org/abs/2504.05258
Topics
language-modellarge-language-modelsllms-reasoningreasoningtemporal-datasettemporal-reasoning
Resources
Readme
MIT-0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
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Stars
14 stars
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Report repository
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Generated from amazon-archives/__template_MIT-0

, 'i');
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// Strip utm_, fbclid, gclid, etc. from all links on page
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Repository files navigation
TISER
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
This repository contains the data for the paper (ACL 2025 Main): Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models.
TISER incorporates a multi-stage inference pipeline that combines explicit reasoning, timeline construction, and iterative self-reflection. The key idea behind our approach is to empower LLMs to adapt by scaling their internal reasoning process during inference. TISER enables models to systematically organize temporal information, verify their inferences, and refine their outputs.
Train Data Format
Each entry in the TISER train dataset is a JSON object containing six fields: dataset_name, question_id, question, answer, prompt, and output. The question field specifies the temporal question being asked, while answer contains the expected short-form response (e.g., an entity or number). The prompt provides detailed instructions for a Chain of Thought (CoT) reasoning process with reflection, guiding the model to reason step-by-step, extract temporal events, reflect on its logic, and produce a final answer. The output field contains the full model-generated response adhering to this reasoning format. This structure supports supervised training of models to perform temporal reasoning and answer generation.
Test Data Format
Each test example in the TISER test dataset is represented as a single JSON object containing five fields: dataset_name, which specifies the split or task; question_id, a unique identifier for the query; question, the temporal reasoning prompt itself; prompt, which embeds the full Chain-of-Thought template (including <reasoning>, <timeline>, <reflection> and <answer> tags) that the model should follow when generating its response; and answer, the held-out ground-truth output against which the model’s <answer> section is evaluated. Unlike the training format, there is no output field in test records, since models read the prompt and produce only an <answer> that is scored directly against the provided answer key.
Data Subsets Provenance
Original data subsets before our preprocessing were extracted from the following HuggingFace URLs
Citation
@misc{bazaga2025learningreasontimetimeline,
title={Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models}, author={Adrián Bazaga and Rexhina Blloshmi and Bill Byrne and Adrià de Gispert},
year={2025},
eprint={2504.05258},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2504.05258}, }

Security
See CONTRIBUTING for more information.
License
This library is licensed under the MIT-0 License. See the LICENSE file.
Contact
For feedback or questions please contact Adrián Bazaga
About
[ACL 2025] Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
arxiv.org/abs/2504.05258
Topics
language-modellarge-language-modelsllms-reasoningreasoningtemporal-datasettemporal-reasoning
Resources
Readme
MIT-0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
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function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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Repository files navigation
TISER
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
This repository contains the data for the paper (ACL 2025 Main): Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models.
TISER incorporates a multi-stage inference pipeline that combines explicit reasoning, timeline construction, and iterative self-reflection. The key idea behind our approach is to empower LLMs to adapt by scaling their internal reasoning process during inference. TISER enables models to systematically organize temporal information, verify their inferences, and refine their outputs.
Train Data Format
Each entry in the TISER train dataset is a JSON object containing six fields: dataset_name, question_id, question, answer, prompt, and output. The question field specifies the temporal question being asked, while answer contains the expected short-form response (e.g., an entity or number). The prompt provides detailed instructions for a Chain of Thought (CoT) reasoning process with reflection, guiding the model to reason step-by-step, extract temporal events, reflect on its logic, and produce a final answer. The output field contains the full model-generated response adhering to this reasoning format. This structure supports supervised training of models to perform temporal reasoning and answer generation.
Test Data Format
Each test example in the TISER test dataset is represented as a single JSON object containing five fields: dataset_name, which specifies the split or task; question_id, a unique identifier for the query; question, the temporal reasoning prompt itself; prompt, which embeds the full Chain-of-Thought template (including <reasoning>, <timeline>, <reflection> and <answer> tags) that the model should follow when generating its response; and answer, the held-out ground-truth output against which the model’s <answer> section is evaluated. Unlike the training format, there is no output field in test records, since models read the prompt and produce only an <answer> that is scored directly against the provided answer key.
Data Subsets Provenance
Original data subsets before our preprocessing were extracted from the following HuggingFace URLs
Citation
@misc{bazaga2025learningreasontimetimeline,
title={Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models}, author={Adrián Bazaga and Rexhina Blloshmi and Bill Byrne and Adrià de Gispert},
year={2025},
eprint={2504.05258},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2504.05258}, }

Security
See CONTRIBUTING for more information.
License
This library is licensed under the MIT-0 License. See the LICENSE file.
Contact
For feedback or questions please contact Adrián Bazaga
About
[ACL 2025] Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
arxiv.org/abs/2504.05258
Topics
language-modellarge-language-modelsllms-reasoningreasoningtemporal-datasettemporal-reasoning
Resources
Readme
MIT-0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
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Custom properties
Stars
14 stars
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0 watching
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5 forks
Report repository
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Used by
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Generated from amazon-archives/__template_MIT-0

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
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TISER
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
This repository contains the data for the paper (ACL 2025 Main): Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models.
TISER incorporates a multi-stage inference pipeline that combines explicit reasoning, timeline construction, and iterative self-reflection. The key idea behind our approach is to empower LLMs to adapt by scaling their internal reasoning process during inference. TISER enables models to systematically organize temporal information, verify their inferences, and refine their outputs.
Train Data Format
Each entry in the TISER train dataset is a JSON object containing six fields: dataset_name, question_id, question, answer, prompt, and output. The question field specifies the temporal question being asked, while answer contains the expected short-form response (e.g., an entity or number). The prompt provides detailed instructions for a Chain of Thought (CoT) reasoning process with reflection, guiding the model to reason step-by-step, extract temporal events, reflect on its logic, and produce a final answer. The output field contains the full model-generated response adhering to this reasoning format. This structure supports supervised training of models to perform temporal reasoning and answer generation.
Test Data Format
Each test example in the TISER test dataset is represented as a single JSON object containing five fields: dataset_name, which specifies the split or task; question_id, a unique identifier for the query; question, the temporal reasoning prompt itself; prompt, which embeds the full Chain-of-Thought template (including <reasoning>, <timeline>, <reflection> and <answer> tags) that the model should follow when generating its response; and answer, the held-out ground-truth output against which the model’s <answer> section is evaluated. Unlike the training format, there is no output field in test records, since models read the prompt and produce only an <answer> that is scored directly against the provided answer key.
Data Subsets Provenance
Original data subsets before our preprocessing were extracted from the following HuggingFace URLs
Citation
@misc{bazaga2025learningreasontimetimeline,
title={Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models}, author={Adrián Bazaga and Rexhina Blloshmi and Bill Byrne and Adrià de Gispert},
year={2025},
eprint={2504.05258},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2504.05258}, }

Security
See CONTRIBUTING for more information.
License
This library is licensed under the MIT-0 License. See the LICENSE file.
Contact
For feedback or questions please contact Adrián Bazaga
About
[ACL 2025] Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
arxiv.org/abs/2504.05258
Topics
language-modellarge-language-modelsllms-reasoningreasoningtemporal-datasettemporal-reasoning
Resources
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MIT-0 license
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Code of conduct
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Contributing
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Repository files navigation
TISER
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
This repository contains the data for the paper (ACL 2025 Main): Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models.
TISER incorporates a multi-stage inference pipeline that combines explicit reasoning, timeline construction, and iterative self-reflection. The key idea behind our approach is to empower LLMs to adapt by scaling their internal reasoning process during inference. TISER enables models to systematically organize temporal information, verify their inferences, and refine their outputs.
Train Data Format
Each entry in the TISER train dataset is a JSON object containing six fields: dataset_name, question_id, question, answer, prompt, and output. The question field specifies the temporal question being asked, while answer contains the expected short-form response (e.g., an entity or number). The prompt provides detailed instructions for a Chain of Thought (CoT) reasoning process with reflection, guiding the model to reason step-by-step, extract temporal events, reflect on its logic, and produce a final answer. The output field contains the full model-generated response adhering to this reasoning format. This structure supports supervised training of models to perform temporal reasoning and answer generation.
Test Data Format
Each test example in the TISER test dataset is represented as a single JSON object containing five fields: dataset_name, which specifies the split or task; question_id, a unique identifier for the query; question, the temporal reasoning prompt itself; prompt, which embeds the full Chain-of-Thought template (including <reasoning>, <timeline>, <reflection> and <answer> tags) that the model should follow when generating its response; and answer, the held-out ground-truth output against which the model’s <answer> section is evaluated. Unlike the training format, there is no output field in test records, since models read the prompt and produce only an <answer> that is scored directly against the provided answer key.
Data Subsets Provenance
Original data subsets before our preprocessing were extracted from the following HuggingFace URLs
Citation
@misc{bazaga2025learningreasontimetimeline,
title={Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models}, author={Adrián Bazaga and Rexhina Blloshmi and Bill Byrne and Adrià de Gispert},
year={2025},
eprint={2504.05258},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2504.05258}, }

Security
See CONTRIBUTING for more information.
License
This library is licensed under the MIT-0 License. See the LICENSE file.
Contact
For feedback or questions please contact Adrián Bazaga
About
[ACL 2025] Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
arxiv.org/abs/2504.05258
Topics
language-modellarge-language-modelsllms-reasoningreasoningtemporal-datasettemporal-reasoning
Resources
Readme
MIT-0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
14 stars
Watchers
0 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Generated from amazon-archives/__template_MIT-0