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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.
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.
Characterizing Information Seeking Events in Health-Related Social Discourse
To the best of our knowledge, this is the first attempt to define event categories for characterizing information-seeking in OUD social discourse.
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.
Scope of Pre-trained Language Models for Detecting Conflicting Health Information
This is the task of health conflict detection (HCD).
Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation
With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2. 75 million sentence pairs, more than 2 million of which were not available before.
