---
title: "Who cites QED: A Framework and Dataset — 48 citing papers"
description: "48 papers of the Elamur Research library cite «QED: A Framework and Dataset for Explanations in Question Answering», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/lamm-2021-qed-framework-dataset-explanations-question/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [QED: A Framework and Dataset for Explanations in…](https://md.research.elamur.ai/free/en/papers/lamm-2021-qed-framework-dataset-explanations-question/) / cited by

# Who cites «QED: A Framework and Dataset for Explanations in Question Answering»

48 papers of our library cite it (of the 141,840 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «QED: A Framework and Dataset for Explanations in Question Answering»](https://md.research.elamur.ai/free/en/papers/lamm-2021-qed-framework-dataset-explanations-question/)

1. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts arXiv:2112.06905**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 479 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
2. [**OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization arXiv:2212.12017**Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, et al.*2022 · 239 citations*](https://md.research.elamur.ai/free/en/papers/iyer-2022-opt-iml-scaling-language-model/)
3. [**Teaching language models to support answers with verified quotes arXiv:2203.11147**Jacob Menick, Maja Trebacz, Vladimir Mikulik, et al.*2022 · 213 citations*](https://md.research.elamur.ai/free/en/papers/menick-2022-teaching-language-models-support-answers/)
4. **[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*
5. **[The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning](https://md.research.elamur.ai/free/en/papers/kim-2023-cot-collection-improving-zero-shot/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Seungone Kim, Se Joo, Doyoung Kim, et al.*2023 · 116 citations*
6. **[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*
7. [**Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering**Najoung Kim, Ellie Pavlick, Burcu Karagol Ayan, et al.*2021 · 63 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-which-linguist-invented-lightbulb-presupposition/)
8. [**Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs arXiv:2402.12030 · Transactions on Machine Learning Research, January 2025**Nicolas Boizard, Kevin El Haddad, Celine Hudelot, et al.*2024 · 34 citations*](https://md.research.elamur.ai/free/en/papers/boizard-2024-towards-cross-tokenizer-distillation-universal/)

40 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/lamm-2021-qed-framework-dataset-explanations-question/) — QED: A Framework and Dataset for Explanations in Question Answering