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Found 7 papers, 1 papers with code
Do LLMs Find Human Answers To Fact-Driven Questions Perplexing? A Case Study on Reddit
Large language models (LLMs) have been shown to be proficient in correctly answering questions in the context of online discourse.
Mad Libs Are All You Need: Augmenting Cross-Domain Document-Level Event Argument Data
Our approach leverages the intuition that Mad Libs, which are categorically masked documents used as a part of a popular game, can be generated and solved by LLMs to produce data for DocEAE.
Statistical Depth for Ranking and Characterizing Transformer-Based Text Embeddings
We adopt a statistical depth to measure distributions of transformer-based text embeddings, transformer-based text embedding (TTE) depth, and introduce the practical use of this depth for both modeling and distributional inference in NLP pipelines.
Text Encoders Lack Knowledge: Leveraging Generative LLMs for Domain-Specific Semantic Textual Similarity
Additionally, we show generative LLMs significantly outperform existing encoder-based STS models when characterizing the semantic similarity between two texts with complex semantic relationships dependent on world knowledge.
The Scope of In-Context Learning for the Extraction of Medical Temporal Constraints
Medications often impose temporal constraints on everyday patient activity.
ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence
It also addresses the challenges of accurately predicting RHBs central to MTCs (e. g., medication intake).
HealthE: Classifying Entities in Online Textual Health Advice
The processing of entities in natural language is essential to many medical NLP systems.
