14 citations · 28 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction
Qingyu Tan, Lu Xu, Lidong Bing +2
The DocRED dataset is one of the most popular and widely used benchmarks for document-level relation extraction (RE). It adopts a recommend-revise annotation scheme so as to have a…
Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation
Qingyu Tan, Ruidan He, Lidong Bing +1
Document-level Relation Extraction (DocRE) is a more challenging task compared to its sentence-level counterpart. It aims to extract relations from multiple sentences at once. In t…
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation
Ruidan He, Linlin Liu, Hai Ye +6
Adapter-based tuning has recently arisen as an alternative to fine-tuning. It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating t…
Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training
Hai Ye, Qingyu Tan, Ruidan He +3
Adapting pre-trained language models (PrLMs) (e.g., BERT) to new domains has gained much attention recently. Instead of fine-tuning PrLMs as done in most previous work, we investig…