--- title: "Who cites Eric Lehman — 318 papers of our library" description: "318 papers of the Elamur Research library cite the works of Eric Lehman, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/eric-lehman/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/eric-lehman/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Eric Lehman](https://md.research.elamur.ai/free/en/authors/eric-lehman/) / cited by # Who cites Eric Lehman 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 · [← Eric Lehman](https://md.research.elamur.ai/free/en/authors/eric-lehman/) 1. [**Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2 arXiv:2311.10702**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/) **[The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://md.research.elamur.ai/free/en/papers/ye-2022-unreliability-explanations-few-shot-prompting/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xi Ye, Greg Durrett*2022 · 196 citations* **[NILE : Natural Language Inference with Faithful Natural Language Explanations](https://md.research.elamur.ai/free/en/papers/kumar-2020-nile-natural-language-inference-faithful/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sawan Kumar, Partha Talukdar*2020 · 182 citations* **[Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-paragraph-level-rationale-extraction-through/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ilias Chalkidis, Manos Fergadiotis, Dimitrios Tsarapatsanis, et al.*2021 · 167 citations* **[Explaining NLP Models via Minimal Contrastive Editing (MiCE)](https://md.research.elamur.ai/free/en/papers/ross-2021-explaining-nlp-models-minimal-contrastive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alexis Ross, Ana Marasović, Matthew Peters*2021 · 154 citations* **[An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction](https://md.research.elamur.ai/free/en/papers/paranjape-2020-information-bottleneck-approach-controlling-conciseness/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bhargavi Paranjape, Mandar Joshi, John Thickstun, et al.*2020 · 137 citations* **[Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?](https://md.research.elamur.ai/free/en/papers/hase-2020-leakage-adjusted-simulatability-can-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Peter Hase, Shiyue Zhang, Harry Xie, et al.*2020 · 120 citations* **[Aligning Faithful Interpretations with their Social Attribution](https://md.research.elamur.ai/free/en/papers/jacovi-2021-aligning-faithful-interpretations-their-social/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Alon Jacovi, Yoav Goldberg*2021 · 119 citations* **[Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students?](https://md.research.elamur.ai/free/en/papers/pruthi-2022-evaluating-explanations-how-much-do/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, et al.*2022 · 107 citations* **[Inseq: An Interpretability Toolkit for Sequence Generation Models](https://md.research.elamur.ai/free/en/papers/sarti-2023-inseq-interpretability-toolkit-sequence-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gabriele Sarti, Nils Feldhus, Ludwig Sickert, et al.*2023 · 96 citations* **[Faithfulness Tests for Natural Language Explanations](https://md.research.elamur.ai/free/en/papers/atanasova-2023-faithfulness-tests-natural-language-explanations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, et al.*2023 · 92 citations* [**Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Understanding arXiv:2205.10687**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/) **[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/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Mobina Mehrazar, Mohammad Amin Yousefi, Parisa Beygi, et al.*2026* **[Review and Performance Comparison of Deep Knowledge Tracing Models (深度知识追踪模型综述和性能比较)](https://md.research.elamur.ai/free/en/papers/wang-2022-performance-comparison-deep/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yu Wang (王宇), Mengxia Zhu (朱梦霞), Shanghui Yang (杨尚辉), et al.*2022* 289 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/eric-lehman/) — Eric Lehman