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20192023
most citedPath-based knowledge reasoning with textual semantic information for medical knowledge graph completion

2 citations · 3 across the 2 of their papers we have counts for

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6 papers · 1 filter

cs.CL20231 cited

Oasis: Data Curation and Assessment System for Pretraining of Large Language Models

Tong Zhou, Yubo Chen, Pengfei Cao +3

Data is one of the most critical elements in building a large language model. However, existing systems either fail to customize a corpus curation pipeline or neglect to leverage c…

cs.CL2023

ZhuJiu: A Multi-dimensional, Multi-faceted Chinese Benchmark for Large Language Models

Baoli Zhang, Haining Xie, Pengfan Du +6

The unprecedented performance of large language models (LLMs) requires comprehensive and accurate evaluation. We argue that for LLMs evaluation, benchmarks need to be comprehensive…

cs.CL2023

LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Models

Yixuan Weng, Zhiqi Wang, Huanxuan Liao +4

With the burgeoning development in the realm of large language models (LLMs), the demand for efficient incremental training tailored to specific industries and domains continues to…

cs.CL2020

Joint Entity and Relation Extraction with Set Prediction Networks

Dianbo Sui, Yubo Chen, Kang Liu +3

The joint entity and relation extraction task aims to extract all relational triples from a sentence. In essence, the relational triples contained in a sentence are unordered. Howe…

cs.CL2019

Copy-Enhanced Heterogeneous Information Learning for Dialogue State Tracking

Qingbin Liu, Shizhu He, Kang Liu +2

Dialogue state tracking (DST) is an essential component in task-oriented dialogue systems, which estimates user goals at every dialogue turn. However, most previous approaches usua…

cs.CL2019

CBOWRA: A Representation Learning Approach for Medication Anomaly Detection

Liang Zhao, Zhiyuan Ma, Yangming Zhou +3

Electronic health record is an important source for clinical researches and applications, and errors inevitably occur in the data, which could lead to severe damages to both patien…