Robert L Logan IV — accepted AI-conference papers

2025

Uchaguzi-2022: A Dataset of Citizen Reports on the 2022 Kenyan Election

Roberto Mondini, Neema Kotonya, Robert L Logan IV, Elizabeth M. Olson, Angela Oduor Lungati, Daniel Odongo +5

COLING 2025main

Online reporting platforms have enabled citizens around the world to collectively share their opinions and report in real time on events impacting their local communities. Systematically organizing (e.g., categorizing by attributes) and geotagging large amounts of crowdsourced information is crucial…

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2023

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

Liang Ma, Shuyang Cao, Robert L Logan IV, Di Lu, Shihao Ran, Ke Zhang +2

ACL 2023long

The proliferation of automatic faithfulness metrics for summarization has produced a need for benchmarks to evaluate them. While existing benchmarks measure the correlation with human judgements of faithfulness on model-generated summaries, they are insufficient for diagnosing whether metrics are: 1…

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2022

Continued Pretraining for Better Zero- and Few-Shot Promptability

Zhaofeng Wu, Robert L Logan IV, Pete Walsh, Akshita Bhagia, Dirk Groeneveld, Sameer Singh +1

EMNLP 2022main

Recently introduced language model prompting methods can achieve high accuracy in zero- and few-shot settings while requiring few to no learned task-specific parameters. Nevertheless, these methods still often trail behind full model finetuning. In this work, we investigate if a dedicated continued…

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2022

Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning

Yasaman Razeghi, Robert L Logan IV, Matt Gardner, Sameer Singh

EMNLP 2022finding

Pretrained Language Models (LMs) have demonstrated ability to perform numerical reasoning by extrapolating from a few examples in few-shot settings. However, the extent to which this extrapolation relies on robust reasoning is unclear. In this paper, we investigate how well these models reason with…

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2021

Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference

Robert L Logan IV, Andrew McCallum, Sameer Singh, Dan Bikel

ACL 2021long

Streaming cross document entity coreference (CDC) systems disambiguate mentions of named entities in a scalable manner via incremental clustering. Unlike other approaches for named entity disambiguation (e.g., entity linking), streaming CDC allows for the disambiguation of entities that are unknown…

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