1 citations · 3 across the 9 of their papers we have counts for
9 papers
Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction
Xinhe Li, Jiajun Liu, Peng Wang
Recent studies have demonstrated that Large Language Models (LLMs) have strong mathematical reasoning abilities but rely on hundreds of billions of parameters. To tackle the challe…
Unlearning of Knowledge Graph Embedding via Preference Optimization
Jiajun Liu, Wenjun Ke, Peng Wang +5
Existing knowledge graphs (KGs) inevitably contain outdated or erroneous knowledge that needs to be removed from knowledge graph embedding (KGE) models. To address this challenge,…
Domain-Hierarchy Adaptation via Chain of Iterative Reasoning for Few-shot Hierarchical Text Classification
Ke Ji, Peng Wang, Wenjun Ke +4
Recently, various pre-trained language models (PLMs) have been proposed to prove their impressive performances on a wide range of few-shot tasks. However, limited by the unstructur…
Fast and Continual Knowledge Graph Embedding via Incremental LoRA
Jiajun Liu, Wenjun Ke, Peng Wang +7
Continual Knowledge Graph Embedding (CKGE) aims to efficiently learn new knowledge and simultaneously preserve old knowledge. Dominant approaches primarily focus on alleviating cat…
Towards Continual Knowledge Graph Embedding via Incremental Distillation
Jiajun Liu, Wenjun Ke, Peng Wang +5
Traditional knowledge graph embedding (KGE) methods typically require preserving the entire knowledge graph (KG) with significant training costs when new knowledge emerges. To addr…
Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction
Guozheng Li, Peng Wang, Wenjun Ke +5
Relation extraction (RE) aims to identify relations between entities mentioned in texts. Although large language models (LLMs) have demonstrated impressive in-context learning (ICL…
Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors
Guozheng Li, Peng Wang, Jiajun Liu +4
Relation extraction (RE) is an important task that aims to identify the relationships between entities in texts. While large language models (LLMs) have revealed remarkable in-cont…
Empirical Analysis of Dialogue Relation Extraction with Large Language Models
Guozheng Li, Zijie Xu, Ziyu Shang +3
Dialogue relation extraction (DRE) aims to extract relations between two arguments within a dialogue, which is more challenging than standard RE due to the higher person pronoun fr…
Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction
Guozheng Li, Wenjun Ke, Peng Wang +5
The in-context learning (ICL) for relational triple extraction (RTE) has achieved promising performance, but still encounters two key challenges: (1) how to design effective prompt…