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Language models excel at following instructions but often struggle with the collaborative aspects of conversation that humans naturally employ.
Multiwoz 2.1: Multi-domain dialogue state corrections and state tracking baselines
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Multiwoz - a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling
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Large-scale multi-domain belief tracking with knowledge sharing
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Grounding conversations with improvised dialogues
Hyundong Cho and Jonathan May. 2020 · 2020
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Shrestha Mohanty, Negar Arabzadeh, Julia Kiseleva, Artem Zholus, Milagro Teruel, Ahmed Awadallah, Yuxuan Sun, Kavya Srinet, and Arthur Szlam. 2023 · 2023
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Soya Park and Chinmay Kulkarni. 2023 · 2023
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A survey on asking clarification questions datasets in conversational systems
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Modeling cross-cultural pragmatic inference with codenames duet
Omar Shaikh, Caleb Ziems, William Held, Aryan Pariani, Fred Morstatter, and Diyi Yang. 2023 · 2023
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Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou. 2023 · 2023
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A long way to go: Investigating length correlations in rlhf
Prasann Singhal, Tanya Goyal, Jiacheng Xu, and Greg Durrett. 2023 · 2023
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Polina Tsvilodub, Michael Franke, Robert D Hawkins, and Noah D Goodman. 2023 · 2023
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Large language models fail on trivial alterations to theory-of-mind tasks
Tomer Ullman. 2023 · 2023
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Clarify when necessary: Resolving ambiguity through interaction with lms
Michael JQ Zhang and Eunsol Choi. 2023 · 2023
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Star-gate: Teaching language models to ask clarifying questions
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Chatbot arena: An open platform for evaluating llms by human preference
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael Jordan, Joseph E Gonzalez, et al. 2024 · 2024
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Clarq-llm: A benchmark for models clarifying and requesting information in task-oriented dialog
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Mask your user tokens
Yoni Gottesman. 2024 · 2024
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Instruction following without instruction tuning
John Hewitt, Nelson F Liu, Percy Liang, and Christopher D Manning. 2024 · 2024
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Instruction fine-tuning: Does prompt loss matter?
Mathew Huerta-Enochian and Seung Yong Ko. 2024 · 2024
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Newsinterview: a dataset and a playground to evaluate llms’ ground gap via informational interviews
Michael Lu, Hyundong Justin Cho, Weiyan Shi, Jonathan May, and Alexander Spangher. 2024 · 2024
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When to show a suggestion? integrating human feedback in ai-assisted programming
Hussein Mozannar, Gagan Bansal, Adam Fourney, and Eric Horvitz. 2024 · 2024
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Bridging information gaps in dialogues with grounded exchanges using knowledge graphs
Phillip Schneider, Nektarios Machner, Kristiina Jokinen, and Florian Matthes. 2024 · 2024
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Grounding gaps in language model generations
Omar Shaikh, Kristina Gligorić, Ashna Khetan, Matthias Gerstgrasser, Diyi Yang, and Dan Jurafsky. 2024 · 2024
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Instruction tuning with loss over instructions
Zhengyan Shi, Adam X Yang, Bin Wu, Laurence Aitchison, Emine Yilmaz, and Aldo Lipani. 2024 · 2024
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Imagining a future of designing with ai: Dynamic grounding, constructive negotiation, and sustainable motivation
Priyan Vaithilingam, Ian Arawjo, and Elena L Glassman. 2024 · 2024
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Wildchat: 1m chatgpt interaction logs in the wild
Wenting Zhao, Xiang Ren, Jack Hessel, Claire Cardie, Yejin Choi, and Yuntian Deng. 2024 · 2024
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