---
title: "Who cites Jay DeYoung — 318 papers of our library"
description: "318 papers of the Elamur Research library cite the works of Jay DeYoung, found in their bibliographies; the most cited first."
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---
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# Who cites Jay DeYoung

341 citations of their works from 318 papers of our library (of the 141,840 whose bibliographies we read) · 29 with a page here, the most cited first · [← Jay DeYoung](https://md.research.elamur.ai/free/en/authors/jay-deyoung/)

1. [**ERASER: A Benchmark to Evaluate Rationalized NLP Models**Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, et al.*2020 · 821 citations*](https://md.research.elamur.ai/free/en/papers/deyoung-2020-eraser-benchmark-evaluate-rationalized-nlp/)
2. [**Explainability for Large Language Models: A Survey**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 785 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/)
3. [**Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?**Alon Jacovi, Yoav Goldberg*2020 · 748 citations*](https://md.research.elamur.ai/free/en/papers/jacovi-2020-faithfully-interpretable-nlp-systems-how/)
4. [**Fact or Fiction: Verifying Scientific Claims**David Wadden, Shanchuan Lin, Kyle Lo, et al.*2020 · 706 citations*](https://md.research.elamur.ai/free/en/papers/wadden-2020-fact-or-fiction-verifying-scientific/)
5. [**A Survey on Automated Fact-Checking**Zhijiang Guo, Michael Schlichtkrull, Andreas Vlachos*2022 · 646 citations*](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/)
6. [**A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support**Ashish Sharma, Adam Miner, David Atkins, et al.*2020 · 408 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2020-computational-approach-understanding-empathy-expressed/)
7. [**ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language**Oyvind Tafjord, Bhavana Dalvi, Peter Clark*2021 · 331 citations*](https://md.research.elamur.ai/free/en/papers/tafjord-2021-proofwriter-generating-implications-proofs-abductive/)
8. [**Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?**Peter Hase, Mohit Bansal*2020 · 309 citations*](https://md.research.elamur.ai/free/en/papers/hase-2020-evaluating-explainable-ai-which-algorithmic/)
9. [**Muppet: Massive Multi-task Representations with Pre-Finetuning**Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, et al.*2021 · 299 citations*](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-muppet-massive-multi-task-representations/)
10. [**Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence**Tal Schuster, Adam Fisch, Regina Barzilay*2021 · 289 citations*](https://md.research.elamur.ai/free/en/papers/schuster-2021-get-your-vitamin-c-robust/)
11. [**A Diagnostic Study of Explainability Techniques for Text Classification**Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, et al.*2020 · 273 citations*](https://md.research.elamur.ai/free/en/papers/atanasova-2020-diagnostic-study-explainability-techniques-text/)
12. [**Learning to Faithfully Rationalize by Construction**Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, et al.*2020 · 223 citations*](https://md.research.elamur.ai/free/en/papers/jain-2020-learning-faithfully-rationalize-construction/)
13. [**The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?**Jasmijn Bastings, Katja Filippova*2020 · 219 citations*](https://md.research.elamur.ai/free/en/papers/bastings-2020-elephant-interpretability-room-why-use/)
14. [**Measuring Association Between Labels and Free-Text Rationales**Sarah Wiegreffe, Ana Marasović, Noah A. Smith*2021 · 215 citations*](https://md.research.elamur.ai/free/en/papers/wiegreffe-2021-measuring-association-between-labels-free/)
15. [**Explaining Answers with Entailment Trees**Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, et al.*2021 · 207 citations*](https://md.research.elamur.ai/free/en/papers/dalvi-2021-explaining-answers-entailment-trees/)
16. [**Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2**Hamish Ivison, Yizhong Wang, Valentina Pyatkin, et al.*2023 · 199 citations*](https://md.research.elamur.ai/free/en/papers/ivison-2023-camels-changing-climate-enhancing-lm/)
17. [**The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning**Xi Ye, Greg Durrett*2022 · 196 citations*](https://md.research.elamur.ai/free/en/papers/ye-2022-unreliability-explanations-few-shot-prompting/)
18. [**NILE : Natural Language Inference with Faithful Natural Language Explanations**Sawan Kumar, Partha Talukdar*2020 · 182 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2020-nile-natural-language-inference-faithful/)
19. [**Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases**Ilias Chalkidis, Manos Fergadiotis, Dimitrios Tsarapatsanis, et al.*2021 · 167 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-paragraph-level-rationale-extraction-through/)
20. [**Explaining NLP Models via Minimal Contrastive Editing (MiCE)**Alexis Ross, Ana Marasović, Matthew Peters*2021 · 154 citations*](https://md.research.elamur.ai/free/en/papers/ross-2021-explaining-nlp-models-minimal-contrastive/)
21. [**An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction**Bhargavi Paranjape, Mandar Joshi, John Thickstun, et al.*2020 · 137 citations*](https://md.research.elamur.ai/free/en/papers/paranjape-2020-information-bottleneck-approach-controlling-conciseness/)
22. [**Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?**Peter Hase, Shiyue Zhang, Harry Xie, et al.*2020 · 120 citations*](https://md.research.elamur.ai/free/en/papers/hase-2020-leakage-adjusted-simulatability-can-models/)
23. [**Aligning Faithful Interpretations with their Social Attribution**Alon Jacovi, Yoav Goldberg*2021 · 119 citations*](https://md.research.elamur.ai/free/en/papers/jacovi-2021-aligning-faithful-interpretations-their-social/)
24. [**Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students?**Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, et al.*2022 · 107 citations*](https://md.research.elamur.ai/free/en/papers/pruthi-2022-evaluating-explanations-how-much-do/)
25. [**Inseq: An Interpretability Toolkit for Sequence Generation Models**Gabriele Sarti, Nils Feldhus, Ludwig Sickert, et al.*2023 · 96 citations*](https://md.research.elamur.ai/free/en/papers/sarti-2023-inseq-interpretability-toolkit-sequence-generation/)
26. [**Faithfulness Tests for Natural Language Explanations**Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/atanasova-2023-faithfulness-tests-natural-language-explanations/)
27. [**Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Understanding**Abbas Ghaddar, Yimeng Wu, Sunyam Bagga, et al.*2022 · 9 citations*](https://md.research.elamur.ai/free/en/papers/ghaddar-2022-revisiting-pre-trained-language-models-their/)
28. [**Can LLMs Faithfully Explain Themselves in Low-Resource Languages? A Case Study on Emotion Detection in Persian**Mobina Mehrazar, Mohammad Amin Yousefi, Parisa Beygi, et al.*2026*](https://md.research.elamur.ai/free/en/papers/mehrazar-2026-can-llms-faithfully-explain-themselves/)
29. [**Review and Performance Comparison of Deep Knowledge Tracing Models (深度知识追踪模型综述和性能比较)**Yu Wang (王宇), Mengxia Zhu (朱梦霞), Shanghui Yang (杨尚辉), et al.*2022*](https://md.research.elamur.ai/free/en/papers/wang-2022-performance-comparison-deep/)

289 more citing papers of our library have no page here yet.

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