Conversational User-AI Intervention: A Study on Prompt Rewriting for Improved LLM Response Generation

Offline reinforcement learning from human feedback in real-world sequence-to-sequence tasks

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Earlier work this paper cites.

Learning a voice-based conversational recommender using offline policy optimization

Francois Mairesse, Zhonghao Luo, and Tao Ye. 2021 · 2021

Earlier work this paper cites.

Characterizing stage-aware writing assistance for collaborative document authoring

Bahareh Sarrafzadeh, Sujay Kumar Jauhar, Michael Gamon, Edward Lank, and Ryen W. White. 2021 · 2021

Earlier work this paper cites.

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Earlier work this paper cites.

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Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, and Jared Kaplan. 2022 · 2022

Earlier work this paper cites.

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Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022

Earlier work this paper cites.

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Original

Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, and Igor Mordatch. 2023 · 2023

Earlier work this paper cites.

Natural language decompositions of implicit content enable better text representations

Alexander Hoyle, Rupak Sarkar, Pranav Goel, and Philip Resnik. 2023 · 2023

Earlier work this paper cites.

Multi-dimensional evaluation of text summarization with in-context learning

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Sameer Jain, Vaishakh Keshava, Swarnashree Mysore Sathyendra, Patrick Fernandes, Pengfei Liu, Graham Neubig, and Chunting Zhou. 2023 · 2023

Earlier work this paper cites.

Query rewriting in retrieval-augmented large language models

Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. 2023 · 2023

Earlier work this paper cites.

FollowupQG: Towards information-seeking follow-up question generation

Yan Meng, Liangming Pan, Yixin Cao, and Min-Yen Kan. 2023 · 2023

Earlier work this paper cites.

Overview of robust and multilingual automatic evaluation metricsfor open-domain dialogue systems at DSTC 11 track 4

Mario Rodríguez-Cantelar, Chen Zhang, Chengguang Tang, Ke Shi, Sarik Ghazarian, João Sedoc, Luis Fernando D’Haro, and Alexander I. Rudnicky. 2023 · 2023

Earlier work this paper cites.

Why johnny can’t prompt: How non-ai experts try (and fail) to design llm prompts

J.D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, and Qian Yang. 2023 · 2023

Earlier work this paper cites.

Llmeval: A preliminary study on how to evaluate large language models

Original

Yue Zhang, Ming Zhang, Haipeng Yuan, Shichun Liu, Yongyao Shi, Tao Gui, Qi Zhang, and Xuanjing Huang. 2023 · 2023

Earlier work this paper cites.

Judging llm-as-a-judge with mt-bench and chatbot arena

Original

Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023 · 2023

Earlier work this paper cites.

StudentEval: A benchmark of student-written prompts for large language models of code

Hannah McLean Babe, Sydney Nguyen, Yangtian Zi, Arjun Guha, Molly Q Feldman, and Carolyn Jane Anderson. 2024 · 2024

Earlier work this paper cites.

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Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, and Anirudh Goyal et. al. 2024 · 2024

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Why and when llm-based assistants can go wrong: Investigating the effectiveness of prompt-based interactions for software help-seeking

Anjali Khurana, Hariharan Subramonyam, and Parmit K Chilana. 2024 · 2024

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Open source language models can provide feedback: Evaluating llms’ ability to help students using gpt-4-as-a-judge

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Charles Koutcheme, Nicola Dainese, Sami Sarsa, Arto Hellas, Juho Leinonen, and Paul Denny. 2024 · 2024

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Learning to rewrite prompts for personalized text generation

Cheng Li, Mingyang Zhang, Qiaozhu Mei, Weize Kong, and Michael Bendersky. 2024a · 2024

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Leveraging large language models for NLG evaluation: Advances and challenges

Wildfeedback: Aligning llms with in-situ user interactions and feedback

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Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Jauhar, Xiaofeng Xu, Xia Song, and Jennifer Neville. 2024 · 2024

Later among the works it cites.

Are large language models capable of generating human-level narratives?

Yufei Tian, Tenghao Huang, Miri Liu, Derek Jiang, Alexander Spangher, Muhao Chen, Jonathan May, and Nanyun Peng. 2024 · 2024

Later among the works it cites.

Tnt-llm: Text mining at scale with large language models

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Later among the works it cites.

Understanding user experience in large language model interactions

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Jiayin Wang, Weizhi Ma, Peijie Sun, Min Zhang, and Jian-Yun Nie. 2024 · 2024

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Hallucination is inevitable: An innate limitation of large language models