Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuning

Understand

Knowledge in the real world is being updated constantly.

• However, it is costly to frequently update large language models (LLMs).
• Therefore, it is crucial for LLMs to understand the concept of temporal knowledge.
• However, prior works on temporal question answering (TQA) did not emphasize multi-answer and multi-hop types of temporal reasoning.