--- title: "Who cites Avoiding Reasoning Shortcuts: — 57 citing papers" description: "57 papers of the Elamur Research library cite «Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for…" canonical: "https://research.elamur.ai/free/en/papers/jiang-2019-avoiding-reasoning-shortcuts-adversarial-evaluation/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/jiang-2019-avoiding-reasoning-shortcuts-adversarial-evaluation/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Avoiding Reasoning Shortcuts: Adversarial…](https://md.research.elamur.ai/free/en/papers/jiang-2019-avoiding-reasoning-shortcuts-adversarial-evaluation/) / cited by # Who cites «Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA» 57 papers of our library cite it (of the 141,840 whose bibliographies we read) · 17 with a page here, the most cited first · [← back to «Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA»](https://md.research.elamur.ai/free/en/papers/jiang-2019-avoiding-reasoning-shortcuts-adversarial-evaluation/) 1. [**Red Teaming Language Models with Language Models**Ethan Perez, Saffron Huang, Francis Song, et al.*2022 · 666 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-red-teaming-language-models-language/) 2. [**Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned**Deep Ganguli, Liane Lovitt, Jackson Kernion, et al.*2022 · 460 citations*](https://md.research.elamur.ai/free/en/papers/ganguli-2022-red-teaming-language-models-reduce/) 3. [**An Empirical Survey of Data Augmentation for Limited Data Learning in NLP**Jiaao Chen, Derek Tam, Colin Raffel, et al.*2023 · 253 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-empirical-survey-data-augmentation-limited/) 4. [**Hierarchical Graph Network for Multi-hop Question Answering**Yuwei Fang, Siqi Sun, Zhe Gan, et al.*2020 · 241 citations*](https://md.research.elamur.ai/free/en/papers/fang-2020-hierarchical-graph-network-multi-hop/) 5. [**TrustLLM: Trustworthiness in Large Language Models**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/) 6. [**HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification**Yichen Jiang, Shikha Bordia, Zheng Zhong, et al.*2020 · 221 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2020-hover-dataset-many-hop-fact/) 7. [**Break It Down: A Question Understanding Benchmark**Tomer Wolfson, Mor Geva, Ankit Gupta, et al.*2020 · 218 citations*](https://md.research.elamur.ai/free/en/papers/wolfson-2020-break-it-down-question-understanding/) 8. [**Unsupervised Question Decomposition for Question Answering**Ethan Perez, Patrick Lewis, Wen-tau Yih, et al.*2020 · 218 citations*](https://md.research.elamur.ai/free/en/papers/perez-2020-unsupervised-question-decomposition-question-answering/) 9. [**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/) 10. [**QuALITY: Question Answering with Long Input Texts, Yes!**Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, et al.*2022 · 162 citations*](https://md.research.elamur.ai/free/en/papers/pang-2022-quality-question-answering-long-input/) 11. [**ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning**Yujia Qin, Yankai Lin, Ryuichi Takanobu, et al.*2021 · 139 citations*](https://md.research.elamur.ai/free/en/papers/qin-2021-erica-improving-entity-relation-understanding/) 12. [**MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data**Yilun Zhao, Yunxiang Li, Chenying Li, et al.*2022 · 128 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2022-multihiertt-numerical-reasoning-over-multi/) 13. [**Efficient Long-Text Understanding with Short-Text Models**Maor Ivgi, Uri Shaham, Jonathan Berant*2023 · 93 citations*](https://md.research.elamur.ai/free/en/papers/ivgi-2023-efficient-long-text-understanding-short/) 14. [**Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI**Jidong Tian, Yitian Li, Wenqing Chen, et al.*2021 · 86 citations*](https://md.research.elamur.ai/free/en/papers/tian-2021-diagnosing-first-order-logical-reasoning/) 15. [**Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models**Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schlegel-2020-leaderboards-survey-methods-revealing-weaknesses/) 16. [**Robustifying Multi-hop QA through Pseudo-Evidentiality Training**Kyungjae Lee, Seung-won Hwang, Sang-eun Han, et al.*2021 · 19 citations*](https://md.research.elamur.ai/free/en/papers/lee-2021-robustifying-multi-hop-qa-through/) 17. [**CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps**Jungmin Yun, June Hyoung Kwon, Youngbin Kim*2026*](https://md.research.elamur.ai/free/en/papers/yun-2026-crit-qa-evaluating-multi-hop/) 40 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/jiang-2019-avoiding-reasoning-shortcuts-adversarial-evaluation/) — Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA