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Large language models in medical and healthcare fields: applications, advances, and challenges
Large language models (LLMs) are increasingly recognized for their advanced language
capabilities, offering significant assistance in diverse areas like medical communication …
capabilities, offering significant assistance in diverse areas like medical communication …
SemEval-2023 task 7: Multi-evidence natural language inference for clinical trial data
This paper describes the results of SemEval 2023 task 7--Multi-Evidence Natural Language
Inference for Clinical Trial Data (NLI4CT)--consisting of 2 tasks, a Natural Language …
Inference for Clinical Trial Data (NLI4CT)--consisting of 2 tasks, a Natural Language …
Several categories of large language models (llms): A short survey
Large Language Models (LLMs) have become effective tools for natural language
processing and have been used in many different fields. This essay offers a succinct …
processing and have been used in many different fields. This essay offers a succinct …
To the cutoff... and beyond? a longitudinal perspective on llm data contamination
Recent claims about the impressive abilities of large language models (LLMs) are often
supported by evaluating publicly available benchmarks. Since LLMs train on wide swaths of …
supported by evaluating publicly available benchmarks. Since LLMs train on wide swaths of …
NLI4CT: Multi-evidence natural language inference for clinical trial reports
How can we interpret and retrieve medical evidence to support clinical decisions? Clinical
trial reports (CTR) amassed over the years contain indispensable information for the …
trial reports (CTR) amassed over the years contain indispensable information for the …
Natural language inference model for customer advocacy detection in online customer engagement
Online customer advocacy has developed as a distinctive strategic way to improve
organisational performance by fostering favourable reciprocal affinitive customer behaviours …
organisational performance by fostering favourable reciprocal affinitive customer behaviours …
How often are errors in natural language reasoning due to paraphrastic variability?
Large language models have been shown to behave inconsistently in response to meaning-
preserving paraphrastic inputs. At the same time, researchers evaluate the knowledge and …
preserving paraphrastic inputs. At the same time, researchers evaluate the knowledge and …
Partial-input baselines show that NLI models can ignore context, but they don't
When strong partial-input baselines reveal artifacts in crowdsourced NLI datasets, the
performance of full-input models trained on such datasets is often dismissed as reliance on …
performance of full-input models trained on such datasets is often dismissed as reliance on …
Understanding and mitigating spurious correlations in text classification with neighborhood analysis
Recent research has revealed that machine learning models have a tendency to leverage
spurious correlations that exist in the training set but may not hold true in general …
spurious correlations that exist in the training set but may not hold true in general …
[HTML][HTML] Exploring named entity recognition and relation extraction for ontology and medical records integration
The available natural language data in electronic health records is of noteworthy interest to
health research and development. Nevertheless, their manual analysis is not feasible and …
health research and development. Nevertheless, their manual analysis is not feasible and …