--- title: "Who cites A Unified Approach to Interpreting Model…" description: "616 papers of the Elamur Research library cite «A Unified Approach to Interpreting Model Predictions», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/lundberg-2017-unified-approach-interpreting-model-predictions/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/lundberg-2017-unified-approach-interpreting-model-predictions/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [A Unified Approach to Interpreting Model…](https://md.research.elamur.ai/free/en/papers/lundberg-2017-unified-approach-interpreting-model-predictions/) / cited by # Who cites «A Unified Approach to Interpreting Model Predictions» 616 papers of our library cite it (of the 140884 whose bibliographies we read) · 25 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/lundberg-2017-unified-approach-interpreting-model-predictions/) - [On the Opportunities and Risks of Foundation Models](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/) — Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al. · 2021 · 3 044 citations - [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/) — Bonan Min, Hayley Ross, Elior Sulem, et al. · 2021 · 1 548 citations - [Bias and Fairness in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/) — Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al. · 2023 · 1 072 citations - [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://md.research.elamur.ai/free/en/papers/deyoung-2020-eraser-benchmark-evaluate-rationalized-nlp/) — Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, et al. · 2020 · 820 citations - [Explainability for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/) — Haiyan Zhao, Hanjie Chen, Fan Yang, et al. · 2023 · 785 citations - [Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?](https://md.research.elamur.ai/free/en/papers/jacovi-2020-faithfully-interpretable-nlp-systems-how/) — Alon Jacovi, Yoav Goldberg · 2020 · 747 citations - [Security and Privacy Challenges of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/das-2024-security-privacy-challenges-large-language/) — Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu · 2024 · 422 citations - [A Diagnostic Study of Explainability Techniques for Text Classification](https://md.research.elamur.ai/free/en/papers/atanasova-2020-diagnostic-study-explainability-techniques-text/) — Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, et al. · 2020 · 273 citations - [Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models](https://md.research.elamur.ai/free/en/papers/wu-2021-polyjuice-generating-counterfactuals-explaining-evaluating/) — Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, et al. · 2021 · 271 citations - [Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/) — Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li · 2023 · 219 citations - [Towards Faithful Model Explanation in NLP: A Survey](https://md.research.elamur.ai/free/en/papers/lyu-2024-faithful-model-explanation-nlp-survey/) — Qing Lyu, Marianna Apidianaki, Chris Callison-Burch · 2024 · 194 citations - [Supervised and Unsupervised Neural Approaches to Text Readability](https://md.research.elamur.ai/free/en/papers/martinc-2021-supervised-unsupervised-neural-approaches-text/) — Matej Martinc, Senja Pollak, Marko Robnik-Šikonja · 2021 · 190 citations - [SemEval-2021 Task 5: Toxic Spans Detection](https://md.research.elamur.ai/free/en/papers/pavlopoulos-2021-semeval-2021-task-5-toxic/) — John Pavlopoulos, Jeffrey Sorensen, Léo Laugier, et al. · 2021 · 172 citations - [Multilingual and Explainable Text Detoxification with Parallel Corpora](https://md.research.elamur.ai/free/en/papers/dementieva-2025-multilingual-explainable-text-detoxification-parallel/) — Daryna Dementieva, Nikolay Babakov, Amit Ronen, et al. · 2025 · 19 citations - ["You might think about slightly revising the title": Identifying Hedges in Peer-tutoring Interactions](https://md.research.elamur.ai/free/en/papers/raphalen-2022-you-might-think-about-slightly/) — Yann Raphalen, Chloé Clavel, Justine Cassell · 2022 · 15 citations - [Automatic Dialect Density Estimation for African American English](https://md.research.elamur.ai/free/en/papers/johnson-2022-automatic-dialect-density-estimation-african/) — Alexander Johnson, Kevin Everson, Vijay Ravi, et al. · 2022 · 8 citations - [When to generate hedges in peer-tutoring interactions](https://md.research.elamur.ai/free/en/papers/abulimiti-2023-when-generate-hedges-peer-tutoring/) — Alafate Abulimiti, Chloé Clavel, Justine Cassell · 2023 · 4 citations - [Evaluation of Adversarial Robustness in Arabic Language Models](https://md.research.elamur.ai/free/en/papers/alajmi-2026-evaluation-adversarial-robustness-arabic-language/) — Anwar Alajmi, Ayed Salman, Imtiaz Ahmad · 2026 · 1 citation - [Explainable Semantic Textual Similarity via Dissimilar Span Detection](https://md.research.elamur.ai/free/en/papers/lozano-2026-explainable-semantic-textual-similarity-dissimilar/) — Diego Miguel Lozano, Daryna Dementieva, Alexander Fraser · 2026 · 1 citation - [Transparent Semantic Change Detection with Dependency-Based Profiles](https://md.research.elamur.ai/free/en/papers/tat-2026-transparent-semantic-change-detection-dependency/) — Bach Phan Tat, Kris Heylen, Dirk Geeraerts, et al. · 2026 · 1 citation - [Can LLMs Faithfully Explain Themselves in Low-Resource Languages? A Case Study on Emotion Detection in Persian](https://md.research.elamur.ai/free/en/papers/mehrazar-2026-can-llms-faithfully-explain-themselves/) — Mobina Mehrazar, Mohammad Amin Yousefi, Parisa Beygi, et al. · 2026 - [Explore Political Discourse with Transformers. Emergent Paradigmatic and Syntagmatic Representations.](https://md.research.elamur.ai/free/en/papers/vanni-2026-explore-political-discourse-transformers-emergent/) — Laurent Vanni, Damon Mayaffre · 2026 - [Reading Time in the Wild: An Assessment of Readability Predictors Based on Naturally-Observed Reading Times](https://md.research.elamur.ai/free/en/papers/vaals-2026-reading-time-wild-assessment-readability/) — Sijbren van Vaals, Rik van Noord, Malvina Nissim · 2026 - [The Spectrum of Sentiment: Optimistic, Pessimistic, and Neutral Voices in Online Depression Discourse](https://md.research.elamur.ai/free/en/papers/tabusca-2026-spectrum-sentiment-optimistic-pessimistic-neutral/) — Stefana Arina Tabusca, Ana-Maria Bucur, Liviu P. Dinu · 2026 - [Linguistic Features in Automatic Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/ducret-2020-linguistic-features-automatic-sarcasm-detection/) — Martina Ducret, Lauren Kruse, Carlos Martinez, et al. · 2020 591 more papers of our library cite it; they have no page here yet.