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Empathetic response generation is a desirable aspect of conversational agents, crucial for facilitating engaging and emotionally intelligent multi-turn conversations between humans and machines.
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MELD: A multimodal multi-party dataset for emotion recognition in conversations
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Towards empathetic open-domain conversation models: A new benchmark and dataset
Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2019 · 2019
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EmpDG: Multi-resolution interactive empathetic dialogue generation
Qintong Li, Hongshen Chen, Zhaochun Ren, Pengjie Ren, Zhaopeng Tu, and Zhumin Chen. 2020 · 2020
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MIME: MIMicking emotions for empathetic response generation
Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, and Soujanya Poria. 2020a · 2020
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Studying the effects of cognitive biases in evaluation of conversational agents
Sashank Santhanam, Alireza Karduni, and Samira Shaikh. 2020 · 2020
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A computational approach to understanding empathy expressed in text-based mental health support
Ashish Sharma, Adam Miner, David Atkins, and Tim Althoff. 2020 · 2020
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A framework for few-shot language model evaluation
Leo Gao, Jonathan Tow, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Kyle McDonell, Niklas Muennighoff, Jason Phang, Laria Reynolds, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou. 2021 · 2021
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SoulChat: Improving LLMs’ empathy, listening, and comfort abilities through fine-tuning with multi-turn empathy conversations
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A critical analysis of empatheticdialogues as a corpus for empathetic engagement
Alok Debnath and Owen Conlan. 2023 · 2023
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Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou. 2023 · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
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Empathy identification systems are not accurately accounting for context
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Instruction-following evaluation for large language models
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