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Found 5 papers, 4 papers with code
MSciNLI: A Diverse Benchmark for Scientific Natural Language Inference
Furthermore, we show that domain shift degrades the performance of scientific NLI models which demonstrates the diverse characteristics of different domains in our dataset.
DelucionQA: Detecting Hallucinations in Domain-specific Question Answering
Hallucination is a well-known phenomenon in text generated by large language models (LLMs).
Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference
However, despite its substantial success on single sentence classification tasks where the challenge in making use of unlabeled data is to assign "good enough" pseudo-labels, for NLI tasks, the nature of unlabeled data is more complex: one of the sentences in the pair (usually the hypothesis) along with the class label are missing from the data and require human annotations, which makes SSL for NLI more challenging.
Hierarchical Multi-Label Classification of Scientific Documents
For example, a paper can be assigned to several topics in a hierarchy tree.
Hierarchical Multi-label Classification
Multi-Label Text Classification
+2
SciNLI: A Corpus for Natural Language Inference on Scientific Text
Existing Natural Language Inference (NLI) datasets, while being instrumental in the advancement of Natural Language Understanding (NLU) research, are not related to scientific text.
Natural Language Inference
Natural Language Understanding
+1
