Search Results for author: Robert L. Logan IV

Found 14 papers, 8 papers with code

COVIDLies: Detecting COVID-19 Misinformation on Social Media

no code implementations EMNLP (NLP-COVID19) 2020 Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, Sameer Singh

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.

Misconceptions Misinformation +2

BUMP: A Benchmark of Unfaithful Minimal Pairs for Meta-Evaluation of Faithfulness Metrics

1 code implementation20 Dec 2022 Liang Ma, Shuyang Cao, Robert L. Logan IV, Di Lu, Shihao Ran, Ke Zhang, Joel Tetreault, Alejandro Jaimes

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

1 code implementation19 Oct 2022 Zhaofeng Wu, Robert L. Logan IV, Pete Walsh, Akshita Bhagia, Dirk Groeneveld, Sameer Singh, Iz Beltagy

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.

Language Modelling Meta-Learning +1

Impact of Pretraining Term Frequencies on Few-Shot Reasoning

no code implementations15 Feb 2022 Yasaman Razeghi, Robert L. Logan IV, Matt Gardner, Sameer Singh

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

no code implementations NAACL 2022 Robert L. Logan IV, Alexandre Passos, Sameer Singh, Ming-Wei Chang

Textual knowledge bases such as Wikipedia require considerable effort to keep up to date and consistent.

On Importance Sampling-Based Evaluation of Latent Language Models

no code implementations ACL 2020 Robert L. Logan IV, Matt Gardner, Sameer Singh

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

1 code implementation16 Feb 2020 Disi Ji, Robert L. Logan IV, Padhraic Smyth, Mark Steyvers

Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications.

text-classification Text Classification

Knowledge Enhanced Contextual Word Representations

1 code implementation IJCNLP 2019 Matthew E. Peters, Mark Neumann, Robert L. Logan IV, Roy Schwartz, Vidur Joshi, Sameer Singh, Noah A. Smith

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.

Entity Linking Entity Typing +3

PoMo: Generating Entity-Specific Post-Modifiers in Context

no code implementations NAACL 2019 Jun Seok Kang, Robert L. Logan IV, Zewei Chu, Yang Chen, Dheeru Dua, Kevin Gimpel, Sameer Singh, Niranjan Balasubramanian

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

1 code implementation29 Nov 2017 Robert L. Logan IV, Samuel Humeau, Sameer Singh

The broad goal of information extraction is to derive structured information from unstructured data.

Attribute Attribute Extraction

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