amazon-science/TISER
[ACL 2025] Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
This project provides datasets for training and evaluating large language models (LLMs) to answer questions that involve understanding and reasoning about sequences of events over time. It helps improve LLMs' ability to process complex temporal information, organize events into a timeline, and then reflect on their own logic to refine answers. The datasets are designed for researchers and developers working on advanced natural language processing applications where precise temporal understanding is critical.
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Use this if you are a researcher or developer focused on enhancing the temporal reasoning capabilities of large language models for complex question-answering tasks.
Not ideal if you are looking for an out-of-the-box LLM application for end-users, as this project provides data for model development rather than a ready-to-use tool.
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Jun 03, 2025
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