14 citations · 28 across the 7 of their papers we have counts for
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cs.CL2023
Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuning
Qingyu Tan, Hwee Tou Ng, Lidong Bing
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 th…
cs.CL2023★ 1 cited
Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models
Qingyu Tan, Hwee Tou Ng, Lidong Bing
Reasoning about time is of fundamental importance. Many facts are time-dependent. For example, athletes change teams from time to time, and different government officials are elect…
cs.CL2023
Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data
Qingyu Tan, Lu Xu, Lidong Bing +1
Relation extraction (RE) aims to extract relations from sentences and documents. Existing relation extraction models typically rely on supervised machine learning. However, recent…