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Large Language Models have shown exceptional generative abilities in various natural language and generation tasks.
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Deception abilities emerged in large language models
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Widening the pipeline in human-guided reinforcement learning with explanation and context-aware data augmentation
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Planning for proactive assistance in environments with partial observability
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Using knowledge-embedded attention to augment pre-trained language models for fine-grained emotion recognition. In 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII) . IEEE, 1–8
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Trust-Aware Planning: Modeling Trust Evolution in Longitudinal Human-Robot Interaction
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Inner monologue: Embodied reasoning through planning with language models
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Large language models are zero-shot reasoners
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ChatGPT: Jack of all trades, master of none
Jan Kocoń, Igor Cichecki, Oliwier Kaszyca, Mateusz Kochanek, Dominika Szydło, Joanna Baran, Julita Bielaniewicz, Marcin Gruza, Arkadiusz Janz, Kamil Kanclerz, et al · 2023
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Theory of mind may have spontaneously emerged in large language models
Michal Kosinski. 2023a · 2023
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Theory of mind may have spontaneously emerged in large language models
Michal Kosinski. 2023b · 2023
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Llm+ p: Empowering large language models with optimal planning proficiency
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023b · 2023
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" Would I Feel More Secure With a Robot?": Understanding Perceptions of Security Robots in Public Spaces
Gabriela Marcu, Iris Lin, Brandon Williams, Lionel P Robert Jr, and Florian Schaub. 2023 · 2023
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R. Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, and Thomas L. Griffiths. 2023 · 2023
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Clever hans or neural theory of mind? stress testing social reasoning in large language models
Natalie Shapira, Mosh Levy, Seyed Hossein Alavi, Xuhui Zhou, Yejin Choi, Yoav Goldberg, Maarten Sap, and Vered Shwartz. 2023 · 2023
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Large Language Models Can Be Easily Distracted by Irrelevant Context. In Proceedings of the 40th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 202) , Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (Eds.). PMLR, 31210–31227
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H. Chi, Nathanael Schärli, and Denny Zhou. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Large language models fail on trivial alterations to theory-of-mind tasks
Tomer Ullman. 2023b · 2023
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Large Language Models Still Can’t Plan (A Benchmark for LLMs on Planning and Reasoning about Change)
Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. 2023 · 2023
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Exploiting Unlabeled Data for Feedback Efficient Human Preference based Reinforcement Learning
Mudit Verma, Siddhant Bhambri, and Subbarao Kambhampati. 2023 · 2023
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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, and Teven Le Scao et al. 2023 · 2023
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Human Security Robot Interaction and Anthropomorphism: An Examination of Pepper, RAMSEE, and Knightscope Robots
Xin Ye, Lionel Robert, et al · 2023
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A Mental Model Based Theory of Trust
Zahra Zahedi, Sarath Sreedharan, and Subbarao Kambhampati. 2023 · 2023
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A Survey of Large Language Models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen. 2023 · 2023
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ChatGPT can now see, hear, and speak
OpenAI. 2023a · 2024
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