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Large language models have emerged as powerful tools for general intelligence, showcasing advanced natural language processing capabilities that find applications across diverse domains.
Cosmos QA: Machine reading comprehension with contextual commonsense reasoning
Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2019 · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2020
Earlier work this paper cites.
Trl: Transformer reinforcement learning
Leandro von Werra, Younes Belkada, Lewis Tunstall, Edward Beeching, Tristan Thrush, Nathan Lambert, Shengyi Huang, Kashif Rasul, and Quentin Gallouédec. 2020 · 2020
Earlier work this paper cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021
Earlier work this paper cites.
What disease does this patient have? a large-scale open domain question answering dataset from medical exams
Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2021 · 2021
Earlier work this paper cites.
LoRA: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Earlier work this paper cites.
Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering
Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2022 · 2022
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Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023 · 2023
Earlier work this paper cites.
Embracing large language models for medical applications: Opportunities and challenges
Mert Karabacak and Konstantinos Margetis. 2023 · 2023
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Measuring faithfulness in chain-of-thought reasoning
Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner, Carson Denison, Danny Hernandez, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, Kamilė Lukošiūtė, Karina Nguyen, Newton Cheng, Nicholas Joseph, Nicholas Schiefer, Oliver Rausch, Robin Larson, Sam McCandlish, Sandipan Kundu, Saurav Kadavath, Shannon Yang, Thomas Henighan, Timothy Maxwell, Timothy Telleen-Lawton, Tristan Hume, Zac Hatfield-Dodds, Jared Kaplan, Jan Brauner, Samuel R. Bowman, and Ethan Perez. 2023 · 2023
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Prototyping the use of large language models (llms) for adult learning content creation at scale
Daniel Leiker, Sara Finnigan, Ashley Ricker Gyllen, and Mutlu Cukurova. 2023 · 2023
Earlier work this paper cites.
Inference-time intervention: Eliciting truthful answers from a language model
Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. 2023 · 2023
Earlier work this paper cites.
Deductive verification of chain-of-thought reasoning
Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, and Hao Su. 2023 · 2023
Earlier work this paper cites.
Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch. 2023 · 2023
Cited alongside, same era.
Biases in large language models: Origins, inventory, and discussion
Roberto Navigli, Simone Conia, and Björn Ross. 2023 · 2023
Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023 · 2023
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023 · 2023
Cited alongside, same era.
The next chapter: A study of large language models in storytelling
Zhuohan Xie, Trevor Cohn, and Jey Han Lau. 2023 · 2023
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2024 · 2024
Closest in time.
Fine-tuning, quantization, and llms: Navigating unintended outcomes
Divyanshu Kumar, Anurakt Kumar, Sahil Agarwal, and Prashanth Harshangi. 2024 · 2024
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A Survey of Large Language Models in Finance (FinLLMs)
Jean Lee, Nicholas Stevens, Soyeon Caren Han, and Minseok Song. 2024 · 2024
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Rethinking machine unlearning for large language models
Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Yuguang Yao, Chris Yuhao Liu, Xiaojun Xu, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, and Yang Liu. 2024 · 2024
Closest in time.
Injecting new knowledge into large language models via supervised fine-tuning
Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li, Daniel Holstein, Leonardo Nunes, Sara Malvar, Bruno Silva, Ranveer Chandra, Vijay Aski, Pavan Kumar Reddy Yannam, Tolga Aktas, and Todd Hendry. 2024 · 2024
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Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik R Narasimhan. 2023 · 2023
Cited alongside, same era.
Disc-lawllm: Fine-tuning large language models for intelligent legal services
Shengbin Yue, Wei Chen, Siyuan Wang, Bingxuan Li, Chenchen Shen, Shujun Liu, Yuxuan Zhou, Yao Xiao, Song Yun, Xuanjing Huang, and Zhongyu Wei. 2023 · 2023
Cited alongside, same era.
Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, and Ed Chi. 2023 · 2023
Cited alongside, same era.
Abhinav Jauhri et al. Abhimanyu Dubey. 2024 · 2024
Cited alongside, same era.
Faithfulness vs. plausibility: On the (un) reliability of explanations from large language models
Chirag Agarwal, Sree Harsha Tanneru, and Himabindu Lakkaraju. 2024 · 2024
Cited alongside, same era.
Few shot chain-of-thought driven reasoning to prompt llms for open ended medical question answering
Ojas Gramopadhye, Saeel Sandeep Nachane, Prateek Chanda, Ganesh Ramakrishnan, Kshitij Sharad Jadhav, Yatin Nandwani, Dinesh Raghu, and Sachindra Joshi. 2024 · 2024
Cited alongside, same era.
Domain-specialized llm: Financial fine-tuning and utilization method using mistral 7b
Cheonsu Jeong. 2024 · 2024
Cited alongside, same era.
Closest in time.
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2024 · 2024
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Whispered tuning: Data privacy preservation in fine-tuning llms through differential privacy
Tanmay Singh, Harshvardhan Aditya, Vijay Krishna Madisetti, and Arshdeep Bahga. 2024 · 2024
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On the hardness of faithful chain-of-thought reasoning in large language models
Sree Harsha Tanneru, Dan Ley, Chirag Agarwal, and Himabindu Lakkaraju. 2024 · 2024
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Language models don’t always say what they think: unfaithful explanations in chain of thought prompting
Miles Turpin, Julian Michael, Ethan Perez, and Samuel R. Bowman. 2024 · 2024
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Exploring memorization in fine-tuned language models
Shenglai Zeng, Yaxin Li, Jie Ren, Yiding Liu, Han Xu, Pengfei He, Yue Xing, Jiliang Tang, and Dawei Yin. 2024 · 2024
Closest in time.
When scaling meets LLM finetuning: The effect of data, model and finetuning method
Biao Zhang, Zhongtao Liu, Colin Cherry, and Orhan Firat. 2024 · 2024
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Galore: Memory-efficient llm training by gradient low-rank projection
Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, and Yuandong Tian. 2024 · 2024
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