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Found 12 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.
Scope of Large Language Models for Mining Emerging Opinions in Online Health Discourse
We detail (i) a method of claim identification -- the task of identifying if a post title contains a claim and (ii) an opinion mining-driven evaluation framework for stance detection using LLMs.
Chain-of-Thought Embeddings for Stance Detection on Social Media
Chain-of-Thought (COT) prompting has recently been shown to improve performance on stance detection tasks -- alleviating some of these issues.
Not Enough Labeled Data? Just Add Semantics: A Data-Efficient Method for Inferring Online Health Texts
Our error analysis shows that AMR-infused language models perform better on complex texts and generally show less predictive variance in the presence of changing complexity.
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.
Theme-driven Keyphrase Extraction to Analyze Social Media Discourse
Lastly, we found that a large language model (ChatGPT) outperforms unsupervised keyphrase extraction models, and we evaluate its efficacy in this task.
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.
Scope of Pre-trained Language Models for Detecting Conflicting Health Information
This is the task of health conflict detection (HCD).
Single Sample Feature Importance: An Interpretable Algorithm for Low-Level Feature Analysis
Have you ever wondered how your feature space is impacting the prediction of a specific sample in your dataset?
