---
title: "Who cites Samuel Humeau"
description: "331 papers of the Elamur Research library cite the works of Samuel Humeau, found in their bibliographies · page 1 of 2; the most cited first."
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---
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# Who cites Samuel Humeau

354 citations of their works from 331 papers of our library (of the 140 884 whose bibliographies we read) · 43 with a page here, the most cited first · page 1 of 2 · [← Samuel Humeau](https://md.research.elamur.ai/free/en/authors/samuel-humeau/)

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- [Survey of Hallucination in Natural Language Generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/) — Ziwei Ji, Nayeon Lee, Rita Frieske, et al. · 2022 · 5 947 citations
- [Training language models to follow instructions with human feedback](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/) — Long Ouyang, Jeff Wu, Xu Jiang, et al. · 2022 · 5 770 citations
- [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
- [OPT: Open Pre-trained Transformer Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/) — Susan Zhang, Stephen Roller, Naman Goyal, et al. · 2022 · 1 709 citations
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/) — Samuel Gehman, Suchin Gururangan, Maarten Sap, et al. · 2020 · 1 277 citations
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) — Zhiheng Xi, Wenxiang Chen, Xin Guo, et al. · 2023 · 1 262 citations
- [Recipes for Building an Open-Domain Chatbot](https://md.research.elamur.ai/free/en/papers/roller-2021-recipes-building-open-domain-chatbot/) — Stephen Roller, Emily Dinan, Naman Goyal, et al. · 2021 · 1 206 citations
- [PaLM 2 Technical Report](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/) — Rohan Anil, Andrew M. Dai, Orhan Firat, et al. · 2023 · 971 citations
- [Red Teaming Language Models with Language Models](https://md.research.elamur.ai/free/en/papers/perez-2022-red-teaming-language-models-language/) — Ethan Perez, Saffron Huang, Francis Song, et al. · 2022 · 665 citations
- [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://md.research.elamur.ai/free/en/papers/hartvigsen-2022-toxigen-large-scale-machine-generated/) — Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, et al. · 2022 · 571 citations
- [Jailbreaking Black Box Large Language Models in Twenty Queries](https://md.research.elamur.ai/free/en/papers/chao-2023-jailbreaking-black-box-large-language/) — Patrick Chao, Alexander Robey, Edgar Dobriban, et al. · 2023 · 533 citations
- [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://md.research.elamur.ai/free/en/papers/ganguli-2022-red-teaming-language-models-reduce/) — Deep Ganguli, Liane Lovitt, Jackson Kernion, et al. · 2022 · 456 citations
- [Experience Grounds Language](https://md.research.elamur.ai/free/en/papers/bisk-2020-experience-grounds-language/) — Yonatan Bisk, Ari Holtzman, Jesse Thomason, et al. · 2020 · 429 citations
- [AgentBench: Evaluating LLMs as Agents](https://md.research.elamur.ai/free/en/papers/liu-2023-agentbench-evaluating-llms-agents/) — Xiao Liu, Hao Yu, Hanchen Zhang, et al. · 2023 · 404 citations
- [Dialogue Natural Language Inference](https://md.research.elamur.ai/free/en/papers/welleck-2019-dialogue-natural-language-inference/) — Sean Welleck, Jason Weston, Arthur Szlam, et al. · 2019 · 382 citations
- [Tool Learning with Foundation Models](https://md.research.elamur.ai/free/en/papers/qin-2023-tool-learning-foundation-models/) — Yujia Qin, Shengding Hu, Yankai Lin, et al. · 2023 · 376 citations
- [Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations](https://md.research.elamur.ai/free/en/papers/inan-2023-llama-guard-llm-based-input/) — Hakan Inan, Kartikeya Upasani, Jianfeng Chi, et al. · 2023 · 369 citations
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://md.research.elamur.ai/free/en/papers/sap-2022-annotators-attitudes-how-annotator-beliefs/) — Maarten Sap, Swabha Swayamdipta, Laura Vianna, et al. · 2022 · 364 citations
- [Improving alignment of dialogue agents via targeted human judgements](https://md.research.elamur.ai/free/en/papers/glaese-2022-improving-alignment-dialogue-agents-targeted/) — Amelia Glaese, Nat McAleese, Maja Trębacz, et al. · 2022 · 346 citations
- [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://md.research.elamur.ai/free/en/papers/vidgen-2021-learning-worst-dynamically-generated-datasets/) — Bertie Vidgen, Tristan Thrush, Zeerak Waseem, et al. · 2021 · 320 citations
- [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/) — Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al. · 2022 · 312 citations
- [MoEL: Mixture of Empathetic Listeners](https://md.research.elamur.ai/free/en/papers/lin-2019-moel-mixture-empathetic-listeners/) — Zhaojiang Lin, Andrea Madotto, Jamin Shin, et al. · 2019 · 301 citations
- [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://md.research.elamur.ai/free/en/papers/smith-2020-can-you-put-it-all/) — Eric Michael Smith, Mary Williamson, Kurt Shuster, et al. · 2020 · 288 citations
- [ConveRT: Efficient and Accurate Conversational Representations from Transformers](https://md.research.elamur.ai/free/en/papers/henderson-2020-convert-efficient-accurate-conversational-representations/) — Matthew Henderson, Iñigo Casanueva, Nikola Mrkšić, et al. · 2020 · 267 citations
- [Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation](https://md.research.elamur.ai/free/en/papers/dinan-2020-queens-are-powerful-too-mitigating/) — Emily Dinan, Angela Fan, Adina Williams, et al. · 2020 · 265 citations

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