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Less defined knowledge and more true alarms: Reference-based phishing detection without a pre-defined reference list
Phishing, a pervasive form of social engineering attack that compromises user credentials,
has led to significant financial losses and undermined public trust. Modern phishing …
has led to significant financial losses and undermined public trust. Modern phishing …
Atomic Inference for NLI with Generated Facts as Atoms
With recent advances, neural models can achieve human-level performance on various
natural language tasks. However, there are no guarantees that any explanations from these …
natural language tasks. However, there are no guarantees that any explanations from these …
EXCGEC: A Benchmark of Edit-wise Explainable Chinese Grammatical Error Correction
Existing studies explore the explainability of Grammatical Error Correction (GEC) in a limited
scenario, where they ignore the interaction between corrections and explanations. To bridge …
scenario, where they ignore the interaction between corrections and explanations. To bridge …
Edinburgh Clinical NLP at SemEval-2024 Task 2: Fine-tune your model unless you have access to GPT-4
The NLI4CT task assesses Natural Language Inference systems in predicting whether
hypotheses entail or contradict evidence from Clinical Trial Reports. In this study, we …
hypotheses entail or contradict evidence from Clinical Trial Reports. In this study, we …
Enhancing adversarial robustness in Natural Language Inference using explanations
The surge of state-of-the-art Transformer-based models has undoubtedly pushed the limits
of NLP model performance, excelling in a variety of tasks. We cast the spotlight on the …
of NLP model performance, excelling in a variety of tasks. We cast the spotlight on the …
Edinburgh Clinical NLP at MEDIQA-CORR 2024: Guiding Large Language Models with Hints
The MEDIQA-CORR 2024 shared task aims to assess the ability of Large Language Models
(LLMs) to identify and correct medical errors in clinical notes. In this study, we evaluate the …
(LLMs) to identify and correct medical errors in clinical notes. In this study, we evaluate the …
Enhancing In-Context Learning with Semantic Representations for Relation Extraction
In this work, we employ two AMR-enhanced semantic representations for ICL on RE: one
that explores the AMR structure generated for a sentence at the subgraph level (shortest …
that explores the AMR structure generated for a sentence at the subgraph level (shortest …