🔗 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) · 9 with a page here, the most cited first · ← back to «QED: A Framework and Dataset for Explanations in Question Answering»

1. Scaling Instruction-Finetuned Language ModelsarXiv:2210.11416Hyung Won Chung, Le Hou, Shayne Longpre, et al.2022 · 3,339 citations
2. GLaM: Efficient Scaling of Language Models with Mixture-of-ExpertsarXiv:2112.06905Nan Du, Yanping Huang, Andrew M. Dai, et al.2021 · 479 citations
3. OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of GeneralizationarXiv:2212.12017Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, et al.2022 · 239 citations
4. Teaching language models to support answers with verified quotesarXiv:2203.11147Jacob Menick, Maja Trebacz, Vladimir Mikulik, et al.2022 · 213 citations
   The Unreliability of Explanations in Few-shot Prompting for Textual ReasoningConference on Neural Information Processing Systems (NeurIPS)Xi Ye, Greg Durrett2022 · 196 citations
   The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-TuningConference on Empirical Methods in Natural Language Processing (EMNLP)Seungone Kim, Se Joo, Doyoung Kim, et al.2023 · 116 citations
   Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students?Transactions of the Association for Computational Linguistics (TACL)Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, et al.2022 · 107 citations
5. Which Linguist Invented the Lightbulb? Presupposition Verification for Question-AnsweringNajoung Kim, Ellie Pavlick, Burcu Karagol Ayan, et al.2021 · 63 citations
6. Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMsarXiv:2402.12030 · Transactions on Machine Learning Research, January 2025Nicolas Boizard, Kevin El Haddad, Celine Hudelot, et al.2024 · 34 citations

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

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