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Found 22 papers, 7 papers with code
The Case for a Single Model that can Both Generate Continuations and Fill in the Blank
The task of inserting text into a specified position in a passage, known as fill in the blank (FitB), is useful for a variety of applications where writers interact with a natural language generation (NLG) system to craft text.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
PaLM: Scaling Language Modeling with Pathways
To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
Ranked #1 on
Natural Language Inference
on RTE
Creating Multimedia Summaries Using Tweets and Videos
While popular televised events such as presidential debates or TV shows are airing, people provide commentary on them in real-time.
Quantifying Memorization Across Neural Language Models
Large language models (LMs) have been shown to memorize parts of their training data, and when prompted appropriately, they will emit the memorized training data verbatim.
Counterfactual Memorization in Neural Language Models
Modern neural language models widely used in tasks across NLP risk memorizing sensitive information from their training data.
SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasets
We use our method to curate SynthBio - a new evaluation set for WikiBio - composed of structured attribute lists describing fictional individuals, mapped to natural language biographies.
A Recipe For Arbitrary Text Style Transfer with Large Language Models
In this paper, we leverage large language models (LMs) to perform zero-shot text style transfer.
Wordcraft: a Human-AI Collaborative Editor for Story Writing
As neural language models grow in effectiveness, they are increasingly being applied in real-world settings.
Deduplicating Training Data Makes Language Models Better
As a result, over 1% of the unprompted output of language models trained on these datasets is copied verbatim from the training data.
RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text
In recent years, large neural networks for natural language generation (NLG) have made leaps and bounds in their ability to generate fluent text.
COVI White Paper
Manual contact tracing of Covid-19 cases has significant challenges that limit the ability of public health authorities to minimize community infections.
Toward Better Storylines with Sentence-Level Language Models
We propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives.
Trading Off Diversity and Quality in Natural Language Generation
For open-ended language generation tasks such as storytelling and dialogue, choosing the right decoding algorithm is critical to controlling the tradeoff between generation quality and diversity.
Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs
Importantly, though we discuss potential modifications, this document is not meant as a formal research paper, but instead is a response to some of the privacy characteristics of direct contact tracing apps like TraceTogether and an early-stage Request for Comments to the community.
Cryptography and Security
Automatic Detection of Generated Text is Easiest when Humans are Fooled
Recent advancements in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text.
Comparison of Diverse Decoding Methods from Conditional Language Models
While conditional language models have greatly improved in their ability to output high-quality natural language, many NLP applications benefit from being able to generate a diverse set of candidate sequences.
Unsupervised Hierarchical Story Infilling
Story infilling involves predicting words to go into a missing span from a story.
ChatEval: A Tool for Chatbot Evaluation
We introduce a unified framework for human evaluation of chatbots that augments existing tools and provides a web-based hub for researchers to share and compare their dialog systems.
Learning Translations via Images with a Massively Multilingual Image Dataset
To facilitate research on the task, we introduce a large-scale multilingual corpus of images, each labeled with the word it represents.
Understanding image motion with group representations
Our results demonstrate that this representation is useful for learning motion in the general setting where explicit labels are difficult to obtain.
