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Found 14 papers, 8 papers with code
COVIDLies: Detecting COVID-19 Misinformation on Social Media
The ongoing pandemic has heightened the need for developing tools to flag COVID-19-related misinformation on the internet, specifically on social media such as Twitter.
BUMP: A Benchmark of Unfaithful Minimal Pairs for Meta-Evaluation of Faithfulness Metrics
The proliferation of automatic faithfulness metrics for summarization has produced a need for benchmarks to evaluate them.
Continued Pretraining for Better Zero- and Few-Shot Promptability
We demonstrate that a simple recipe, continued pretraining that incorporates a trainable prompt during multi-task learning, leads to improved promptability in both zero- and few-shot settings compared to existing methods, up to 31% relative.
Impact of Pretraining Term Frequencies on Few-Shot Reasoning
Pretrained Language Models (LMs) have demonstrated ability to perform numerical reasoning by extrapolating from a few examples in few-shot settings.
FRUIT: Faithfully Reflecting Updated Information in Text
Textual knowledge bases such as Wikipedia require considerable effort to keep up to date and consistent.
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models
Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining.
Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ
High-quality and large-scale data are key to success for AI systems.
On Importance Sampling-Based Evaluation of Latent Language Models
In addition, we elucidate subtle differences in how importance sampling is applied in these works that can have substantial effects on the final estimates, as well as provide theoretical results which reinforce the validity of this technique.
Active Bayesian Assessment for Black-Box Classifiers
Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications.
Knowledge Enhanced Contextual Word Representations
Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities.
Ranked #9 on
Relation Classification
on TACRED
Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling
Modeling human language requires the ability to not only generate fluent text but also encode factual knowledge.
PoMo: Generating Entity-Specific Post-Modifiers in Context
Given a sentence about a target entity, the task is to automatically generate a post-modifier phrase that provides contextually relevant information about the entity.
Multimodal Attribute Extraction
The broad goal of information extraction is to derive structured information from unstructured data.
