Papers by Robert L. Logan IV
On Importance Sampling-Based Evaluation of Latent Language Models (2020.acl-main)
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| Challenge: | Existing approaches to evaluate language models using latent structures are intractable as they require marginalizing over the latent space. |
| Approach: | They propose to use importance sampling to evaluate latent language models . they elucidate subtle differences in how importance sampling is applied . |
| Outcome: | The proposed model performs better on tasks requiring structure and interpretability. |
Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ (2020.emnlp-demos)
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Qiang Ning, Hao Wu, Pradeep Dasigi, Dheeru Dua, Matt Gardner, Robert L. Logan IV, Ana Marasović, Zhen Nie
| Challenge: | Efficient data collection is important for advancing research and building time-sensitive applications. |
| Approach: | They propose an open-source platform that standardizes the data collection pipeline . it includes customizable user interface components, automated annotator qualification, and saved pipelines . |
| Outcome: | The proposed platform simplifies data annotation significantly on diverse datasets . it can be used by researchers and engineers to improve reproducibility and minimize overhead . |
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts (2020.emnlp-main)
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| Challenge: | Pretrained language models have been successful when finetuned to downstream tasks . however, it is difficult to determine whether the knowledge that finetuning LMs contain is learned during the pretraining or the finetailing process. |
| Approach: | They propose a method to create prompts for a diverse set of tasks using a gradient-guided search. |
| Outcome: | The proposed method performs sentiment analysis and natural language inference without additional parameters and finetuning. |