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Found 7 papers, 5 papers with code
Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers
We evaluate recent Large language Models (LLMs) on the challenging task of summarizing short stories, which can be lengthy, and include nuanced subtext or scrambled timelines.
Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence
We present a new fact-checking benchmark, Check-COVID, that requires systems to verify claims about COVID-19 from news using evidence from scientific articles.
Unsupervised Selective Rationalization with Noise Injection
To address this problem, unsupervised selective rationalization produces rationales alongside predictions by chaining two jointly-trained components, a rationale generator and a predictor.
Towards Detecting Harmful Agendas in News Articles
Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread.
SafeText: A Benchmark for Exploring Physical Safety in Language Models
Understanding what constitutes safe text is an important issue in natural language processing and can often prevent the deployment of models deemed harmful and unsafe.
Mitigating Covertly Unsafe Text within Natural Language Systems
An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threatening consequences.
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
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