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Found 8 papers, 6 papers with code
ECKGBench: Benchmarking Large Language Models in E-commerce Leveraging Knowledge Graph
To bridge the evaluation gap, we propose ECKGBench, a dataset specifically designed to evaluate the capacities of LLMs in e-commerce knowledge.
ChineseEcomQA: A Scalable E-commerce Concept Evaluation Benchmark for Large Language Models
To address these problems, we propose \textbf{ChineseEcomQA}, a scalable question-answering benchmark focused on fundamental e-commerce concepts.
Cross-domain Named Entity Recognition via Graph Matching
Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario.
ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models
This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs).
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference
Intriguingly, the unified objective can be seen as the sum of the vanilla fine-tuning objective, which learns new knowledge from target data, and the causal objective, which preserves old knowledge from PLMs.
Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition
Thanks to the causal inference, we identify that the forgetting is caused by the missing causal effect from the old data.
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Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification
We then introduce a joint embedding loss and a matching learning loss to model the matching relationship between the text semantics and the label semantics.
MedSRGAN: medical images super-resolution using generative adversarial networks
Super-resolution (SR) in medical imaging is an emerging application in medical imaging due to the needs of high quality images acquired with limited radiation dose, such as low dose Computer Tomography (CT), low field magnetic resonance imaging (MRI).
