---
title: "Shayne Longpre — all works — Elamur Research"
description: "All 34 works of Shayne Longpre in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
canonical: "https://research.elamur.ai/free/en/authors/shayne-longpre/works/"
---
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# Shayne Longpre — all works

34 works · [← Shayne Longpre](https://md.research.elamur.ai/free/en/authors/shayne-longpre/)

1. **[Scaling Instruction-Finetuned Language Models ↗](https://arxiv.org/abs/2210.11416) arXiv:2210.11416 · no summary yet**Hyung Won Chung et al.*2022 · 3,392 citations*
2. **[BLOOM: A 176B-Parameter Open-Access Multilingual Language Model ↗](https://arxiv.org/abs/2211.05100) arXiv:2211.05100 · no summary yet**BigScience Workshop et al.*2022 · 1,606 citations*
3. [**The Flan Collection: Designing Data and Methods for Effective Instruction Tuning arXiv:2301.13688**Shayne Longpre, Le Hou, Tu Vu, et al.*2023 · 607 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/)
4. **[Entity-Based Knowledge Conflicts in Question Answering](https://md.research.elamur.ai/free/en/papers/longpre-2021-entity-based-knowledge-conflicts-question/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shayne Longpre, Kartik Perisetla, Anthony Chen, et al.*2021 · 298 citations*
5. [**Prometheus: Inducing Fine-grained Evaluation Capability in Language Models arXiv:2310.08491**Seungone Kim, Jamin Shin, Yejin Cho, et al.*2023 · 281 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-prometheus-inducing-fine-grained-evaluation/)
6. [**Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models arXiv:2405.01535**Seungone Kim, Juyoung Suk, Shayne Longpre, et al.*2024 · 234 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-prometheus-2-open-source-language/)
7. **[Open-Domain Question Answering Goes Conversational via Question Rewriting](https://md.research.elamur.ai/free/en/papers/anantha-2021-open-domain-question-answering-goes/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, et al.*2021 · 228 citations*
8. **[Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://md.research.elamur.ai/free/en/papers/singh-2025-global-mmlu-understanding-addressing-cultural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shivalika Singh, Angelika Romanou, Clémentine Fourrier, et al.*2025 · 182 citations*
9. **[You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings](https://md.research.elamur.ai/free/en/papers/talat-2022-you-reap-what-you-sow/) [BigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language Models](https://md.research.elamur.ai/free/en/venues/bigscience/)**Zeerak Talat, Aurélie Névéol, Stella Biderman, et al.*2022 · 130 citations*
10. [**A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity arXiv:2305.13169**Shayne Longpre, Gregory Yauney, Emily Reif, et al.*2023 · 104 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-pretrainer-s-guide-training-data/)
11. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/)
12. **[How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers? ↗](https://aclanthology.org/2020.findings-emnlp.394/) Findings of the Association for Computational Linguistics: EMNLP 2020 · no summary yet**Shayne Longpre, Yu Wang, Chris DuBois*2020 · 91 citations*
13. **[The Foundation Model Transparency Index ↗](https://arxiv.org/abs/2310.12941) arXiv:2310.12941 · no summary yet**Rishi Bommasani et al.*2023 · 76 citations*
14. [**Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models arXiv:2305.14705**Sheng Shen, Le Hou, Yanqi Zhou, et al.*2023 · 70 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-mixture-experts-meets-instruction-tuning/)
15. **[The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models ↗](https://aclanthology.org/2025.naacl-long.303/) Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · no summary yet**Seungone Kim et al.*2025 · 57 citations*
16. **[Foundation Model Transparency Reports ↗](https://doi.org/10.1609/aies.v7i1.31628) Proceedings of the AAAI/ACM Conference on AI Ethics and Society · no summary yet**Rishi Bommasani et al.*2024 · 53 citations*
17. **[International AI Safety Report ↗](https://arxiv.org/abs/2501.17805) arXiv (Cornell University) · no summary yet**Yoshua Bengio et al.*2025 · 41 citations*
18. **[Considerations for governing open foundation models ↗](https://doi.org/10.1126/science.adp1848) Science · no summary yet**Rishi Bommasani et al.*2024 · 36 citations*
19. **[On the Societal Impact of Open Foundation Models ↗](https://arxiv.org/abs/2403.07918) arXiv (Cornell University) · no summary yet**Sayash Kapoor et al.*2024 · 30 citations*
20. **[The Leaderboard Illusion ↗](https://arxiv.org/abs/2504.20879) arXiv:2504.20879 · no summary yet**Shivalika Singh et al.*2025 · 22 citations*
21. **[A Safe Harbor for AI Evaluation and Red Teaming ↗](https://arxiv.org/abs/2403.04893) arXiv (Cornell University) · no summary yet**Shayne Longpre et al.*2024 · 22 citations*
22. **[The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text ↗](https://doi.org/10.52202/085713-1918) neural information processing systems · no summary yet**Nikhil Kandpal et al.*2025 · 21 citations*
23. **[MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages ↗](https://aclanthology.org/2022.mia-1.11/) Proceedings of the Workshop on Multilingual Information Access (MIA) · no summary yet**Akari Asai et al.*2022 · 17 citations*
24. **[The Foundation Model Transparency Index v1.1: May 2024 ↗](https://arxiv.org/abs/2407.12929) arXiv (Cornell University) · no summary yet**Rishi Bommasani et al.*2024 · 14 citations*
25. **[International Scientific Report on the Safety of Advanced AI (Interim Report) ↗](https://arxiv.org/abs/2412.05282) arXiv (Cornell University) · no summary yet**Yoshua Bengio et al.*2024 · 13 citations*
26. **[A Way out of the Odyssey: Analyzing and Combining Recent Insights for LSTMs ↗](https://arxiv.org/abs/1611.05104) arXiv (Cornell University) · no summary yet**Shayne Longpre et al.*2016 · 13 citations*
27. **[The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources ↗](https://arxiv.org/abs/2406.16746) arXiv (Cornell University) · no summary yet**Shayne Longpre et al.*2024 · 11 citations*
28. **[The 2025 Foundation Model Transparency Index ↗](https://doi.org/10.70777/si.v2i4.17165) SuperIntelligence - Robotics - Safety & Alignment · no summary yet**Alexander Wan et al.*2026 · 9 citations*
29. **[Establishing Best Practices for Building Rigorous Agentic Benchmarks ↗](https://arxiv.org/abs/2507.02825) arXiv:2507.02825 · no summary yet**Yuxuan Zhu et al.*2025 · 7 citations*
30. **[Position: On the Societal Impact of Open Foundation Models ↗](https://openalex.org/W7135318688) ANU Open Research (Australian National University) · no summary yet**Sayash Kapoor et al.*2024 · 6 citations*
31. **[FlexOLMo: Open Language Models for Flexible Data Use ↗](https://doi.org/10.52202/085713-5530) neural information processing systems · no summary yet**Weijia Shi et al.*2025 · 5 citations*
32. **[Establishing Best Practices in Building Rigorous Agentic Benchmarks ↗](https://doi.org/10.52202/085713-5547) neural information processing systems · no summary yet**Yuxuan Zhu et al.*2025 · 4 citations*
33. **[In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI ↗](https://arxiv.org/abs/2503.16861) arXiv (Cornell University) · no summary yet**Shayne Longpre et al.*2025 · 2 citations*
34. **[FLARE-AI: Flaw Reporting for AI ↗](https://arxiv.org/abs/2606.31567) arXiv (Cornell University) · no summary yet**Shayne Longpre et al.*2026*