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Found 5 papers, 2 papers with code
Explainable Clinical Decision Support from Text
Clinical prediction models often use structured variables and provide outcomes that are not readily interpretable by clinicians.
Standardizing the Measurement of Text Diversity: A Tool and a Comparative Analysis of Scores
The applicability of scores extends beyond analysis of generative models; for example, we highlight applications on instruction-tuning datasets and human-produced texts.
How Much Annotation is Needed to Compare Summarization Models?
Modern instruction-tuned models have become highly capable in text generation tasks such as summarization, and are expected to be released at a steady pace.
Evaluating the Zero-shot Robustness of Instruction-tuned Language Models
To answer the former, we collect a set of 319 instructions manually written by NLP practitioners for over 80 unique tasks included in widely used benchmarks, and we evaluate the variance and average performance of these instructions as compared to instruction phrasings observed during instruction fine-tuning.
Summarizing, Simplifying, and Synthesizing Medical Evidence Using GPT-3 (with Varying Success)
Large language models, particularly GPT-3, are able to produce high quality summaries of general domain news articles in few- and zero-shot settings.
