Fetching the paper…
Reading the bibliography…
AI scientists powered by large language models have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines.
Michael Chui, James Manyika, and David Schwartz. The real-world potential and limitations of artificial intelligence. The McKinsey Quarterly
2018
Earlier work this paper cites.
Stephen Cave and Seán S ÓhÉigeartaigh. Bridging near-and long-term concerns about ai. Nature Machine Intelligence
2019
Earlier work this paper cites.
Sean C. McConnell and Alessandro Blasimme. Ethics, values, and responsibility in human genome editing. AMA Journal of Ethics
2019
Earlier work this paper cites.
Wei Emma Zhang, Quan Z Sheng, Ahoud Alhazmi, and Chenliang Li. Adversarial attacks on deep-learning models in natural language processing: A survey. ACM Transactions on Intelligent Systems and Technology (TIST)
2020
Earlier work this paper cites.
Junyi Wu and Shari Shang. Managing uncertainty in ai-enabled decision making and achieving sustainability. Sustainability
2020
Earlier work this paper cites.
Jose N. Paredes, Juan Carlos L. Teze, Gerardo I. Simari, and Maria Vanina Martinez. On the importance of domain-specific explanations in ai-based cybersecurity systems (technical report), 2021
2021
Earlier work this paper cites.
Alya A Arabi. Artificial intelligence in drug design: algorithms, applications, challenges and ethics. Future Drug Discovery
2021
Earlier work this paper cites.
Zhaoyi Xu and Joseph Homer Saleh. Machine learning for reliability engineering and safety applications: Review of current status and future opportunities. Reliability Engineering & System Safety
2021
Earlier work this paper cites.
Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. Large language models still can’t plan (a benchmark for LLMs on planning and reasoning about change). In NeurIPS 2022 Foundation Models for Decision Making Workshop
2022
Earlier work this paper cites.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. Chain of thought prompting elicits reasoning in large language models. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems
2022
Earlier work this paper cites.
Jin Xu, Xiaojiang Liu, Jianhao Yan, Deng Cai, Huayang Li, and Jian Li. Learning to break the loop: Analyzing and mitigating repetitions for neural text generation. Advances in Neural Information Processing Systems
2022
Earlier work this paper cites.
Thilo Hagendorff and Sarah Fabi. Methodological reflections for ai alignment research using human feedback, 2022
2022
Earlier work this paper cites.
Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan. Webshop: Towards scalable real-world web interaction with grounded language agents. Advances in Neural Information Processing Systems
2022
Earlier work this paper cites.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems
2022
Earlier work this paper cites.
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan. Constitutional ai: Harmlessness from ai feedback, 2022
2022
Earlier work this paper cites.
Sina Mohseni, Haotao Wang, Chaowei Xiao, Zhiding Yu, Zhangyang Wang, and Jay Yadawa. Taxonomy of machine learning safety: A survey and primer. ACM Computing Surveys
2022
Earlier work this paper cites.
Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al. Large language models encode clinical knowledge. Nature
2023
Earlier work this paper cites.
Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. Large language models in medicine. Nature medicine
2023
Earlier work this paper cites.
Daniil A Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes. Autonomous chemical research with large language models. Nature
2023
Earlier work this paper cites.
Murray Shanahan, Kyle McDonell, and Laria Reynolds. Role play with large language models. Nature
2023
Earlier work this paper cites.
Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology
2023
Earlier work this paper cites.
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. CAMEL: Communicative agents for ”mind” exploration of large language model society. In Thirty-seventh Conference on Neural Information Processing Systems
2023
Earlier work this paper cites.
Daniil A. Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes. Autonomous chemical research with large language models. Nature
2023
Earlier work this paper cites.
Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. In Thirty-seventh Conference on Neural Information Processing Systems
2023
Earlier work this paper cites.
Naruki Yoshikawa, Marta Skreta, Kourosh Darvish, Sebastian Arellano-Rubach, Zhi Ji, Lasse Bjorn Kristensen, Andrew Zou Li, Yuchi Zhao, Haoping Xu, Artur Kuramshin, et al. Large language models for chemistry robotics. Autonomous Robots
2023
Earlier work this paper cites.
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. Survey of hallucination in natural language generation. ACM Computing Surveys
2023
Earlier work this paper cites.
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al. A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
2023
Earlier work this paper cites.
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. Jailbroken: How does LLM safety training fail? In Thirty-seventh Conference on Neural Information Processing Systems
2023
Cited alongside, same era.
Jie Huang and Kevin Chen-Chuan Chang. Towards reasoning in large language models: A survey. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Findings of the Association for Computational Linguistics: ACL 2023
2023
Cited alongside, same era.
Michael Wornow, Yizhe Xu, Rahul Thapa, Birju Patel, Ethan Steinberg, Scott Fleming, Michael A Pfeffer, Jason Fries, and Nigam H Shah. The shaky foundations of large language models and foundation models for electronic health records. npj Digital Medicine
2023
Cited alongside, same era.
Anja Thieme, Aditya Nori, Marzyeh Ghassemi, Rishi Bommasani, Tariq Osman Andersen, and Ewa Luger. Foundation models in healthcare: Opportunities, risks & strategies forward. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
Qiao Jin, Yifan Yang, Qingyu Chen, and Zhiyong Lu. Genegpt: Augmenting large language models with domain tools for improved access to biomedical information. Bioinformatics
2024
Closest in time.
Alireza Ghafarollahi and Markus J Buehler. Protagents: protein discovery via large language model multi-agent collaborations combining physics and machine learning. Digital Discovery
2024
Closest in time.
Oliver Bayley, Elia Savino, Aidan Slattery, and Timothy Noël. Autonomous chemistry: Navigating self-driving labs in chemical and material sciences. Matter
2024
Closest in time.
Miles Turpin, Julian Michael, Ethan Perez, and Samuel Bowman. Language models don’t always say what they think: unfaithful explanations in chain-of-thought prompting. Advances in Neural Information Processing Systems
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. Editing large language models: Problems, methods, and opportunities. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
2023
Cited alongside, same era.
Huayang Li, Tian Lan, Zihao Fu, Deng Cai, Lemao Liu, Nigel Collier, Taro Watanabe, and Yixuan Su. Repetition in repetition out: Towards understanding neural text degeneration from the data perspective. Advances in Neural Information Processing Systems
2023
Cited alongside, same era.
Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, and Pete Florence. Palm-e: an embodied multimodal language model. In Proceedings of the 40th International Conference on Machine Learning
2023
Cited alongside, same era.
Qiao Jin, Robert Leaman, and Zhiyong Lu. Retrieve, summarize, and verify: How will chatgpt impact information seeking from the medical literature? Journal of the American Society of Nephrology
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Significant-Gravitas Team. Autogpt: Accessible ai for everyone, 2023. MIT license
2023
Cited alongside, same era.
Silen Naihin, David Atkinson, Marc Green, Merwane Hamadi, Craig Swift, Douglas Schonholtz, Adam Tauman Kalai, and David Bau. Testing language model agents safely in the wild. In Socially Responsible Language Modelling Research
2023
Cited alongside, same era.
Zhexin Zhang, Leqi Lei, Lindong Wu, Rui Sun, Yongkang Huang, Chong Long, Xiao Liu, Xuanyu Lei, Jie Tang, and Minlie Huang. Safetybench: Evaluating the safety of large language models with multiple choice questions, 2023
2023
Cited alongside, same era.
2024
Closest in time.
Junwei Yang, Hanwen Xu, Srbuhi Mirzoyan, Tong Chen, Zixuan Liu, Zequn Liu, Wei Ju, Luchen Liu, Zhiping Xiao, Ming Zhang, et al. Poisoning medical knowledge using large language models. Nature Machine Intelligence
2024
Closest in time.
Shubo Tian, Qiao Jin, Lana Yeganova, Po-Ting Lai, Qingqing Zhu, Xiuying Chen, Yifan Yang, Qingyu Chen, Won Kim, Donald C Comeau, et al. Opportunities and challenges for chatgpt and large language models in biomedicine and health. Briefings in Bioinformatics
2024
Closest in time.
Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris Maddison, and Tatsunori Hashimoto. Identifying the risks of LM agents with an LM-emulated sandbox. In The Twelfth International Conference on Learning Representations (ICLR)
2024
Closest in time.
Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, et al. R-judge: Benchmarking safety risk awareness for llm agents. In Findings of the Association for Computational Linguistics: EMNLP 2024
2024
Closest in time.
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, and Yaodong Yang. Safe rlhf: Safe reinforcement learning from human feedback. In The Twelfth International Conference on Learning Representations
2024
Closest in time.
Yuhui Li, Fangyun Wei, Jinjing Zhao, Chao Zhang, and Hongyang Zhang. Rain: Your language models can align themselves without finetuning. In The Twelfth International Conference on Learning Representations
2024
Closest in time.
Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. Fine-tuning aligned language models compromises safety, even when users do not intend to! In The Twelfth International Conference on Learning Representations
2024
Closest in time.
Xianjun Yang, Xiao Wang, Qi Zhang, Linda Ruth Petzold, William Yang Wang, Xun Zhao, and Dahua Lin. Shadow alignment: The ease of subverting safely-aligned language models. In ICLR 2024 Workshop on Secure and Trustworthy Large Language Models
2024
Closest in time.
Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, Paul Rottger, Dan Jurafsky, Tatsunori Hashimoto, and James Zou. Safety-tuned llamas: Lessons from improving the safety of large language models that follow instructions. In The Twelfth International Conference on Learning Representations
2024
Closest in time.
Mansi Phute, Alec Helbling, Matthew Daniel Hull, ShengYun Peng, Sebastian Szyller, Cory Cornelius, and Duen Horng Chau. Llm self defense: By self examination, llms know they are being tricked. In The Second Tiny Papers Track at ICLR 2024
2024
Closest in time.
Zhexin Zhang, Junxiao Yang, Pei Ke, Fei Mi, Hongning Wang, and Minlie Huang. Defending large language models against jailbreaking attacks through goal prioritization. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
2024
Closest in time.
Bochuan Cao, Yuanpu Cao, Lu Lin, and Jinghui Chen. Defending against alignment-breaking attacks via robustly aligned llm. In 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024
2024
Closest in time.
Adib Hasan, Ileana Rugina, and Alex Wang. Pruning for protection: Increasing jailbreak resistance in aligned llms without fine-tuning, 2024
2024
Closest in time.
Julien Piet, Maha Alrashed, Chawin Sitawarin, Sizhe Chen, Zeming Wei, Elizabeth Sun, Basel Alomair, and David Wagner. Jatmo: Prompt injection defense by task-specific finetuning. In European Symposium on Research in Computer Security
2024
Closest in time.
Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, and others. Planning, creation, usage: Benchmarking llms for comprehensive tool utilization in real-world complex scenarios. In Findings of the Association for Computational Linguistics ACL 2024
2024
Closest in time.
Mengru Wang, Ningyu Zhang, Ziwen Xu, Zekun Xi, Shumin Deng, Yunzhi Yao, Qishen Zhang, Linyi Yang, Jindong Wang, and Huajun Chen. Detoxifying large language models via knowledge editing. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
2024
Closest in time.
Michael Feffer, Anusha Sinha, Wesley H Deng, Zachary C Lipton, and Hoda Heidari. Red-teaming for generative ai: Silver bullet or security theater? In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
2024
Closest in time.
Zhuosheng Zhang, Yao Yao, Aston Zhang, Xiangru Tang, Xinbei Ma, Zhiwei He, Yiming Wang, Mark Gerstein, Rui Wang, Gongshen Liu, et al. Igniting language intelligence: The hitchhiker’s guide from chain-of-thought reasoning to language agents. ACM Computing Surveys
2025
Closest in time.
Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, et al. The rise and potential of large language model based agents: A survey. Science China Information Sciences
2025
Closest in time.
Mayk Caldas Ramos, Christopher J Collison, and Andrew D White. A review of large language models and autonomous agents in chemistry. Chemical Science
2025
Closest in time.
Kourosh Darvish, Marta Skreta, Yuchi Zhao, Naruki Yoshikawa, Sagnik Som, Miroslav Bogdanovic, Yang Cao, Han Hao, Haoping Xu, Alán Aspuru-Guzik, et al. Organa: a robotic assistant for automated chemistry experimentation and characterization. Matter
2025
Closest in time.
Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and others. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems
2025
Closest in time.