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To effectively use large language models (LLMs) for real-world queries, it is imperative that they generalize to the long-tail distribution, i.e.
Availability: A heuristic for judging frequency and probability
Amos Tversky and Daniel Kahneman. 1973 · 1973
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Judgment under uncertainty: Heuristics and biases: Biases in judgments reveal some heuristics of thinking under uncertainty
Amos Tversky and Daniel Kahneman. 1974 · 1974
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Rica: Evaluating robust inference capabilities based on commonsense axioms
Pei Zhou, Rahul Khanna, Seyeon Lee, Bill Yuchen Lin, Daniel Ho, Jay Pujara, and Xiang Ren. 2020 · 2005
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Thinking within the box: The relational processing style elicited by counterfactual mind-sets
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A large annotated corpus for learning natural language inference
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Atomic: An atlas of machine commonsense for if-then reasoning
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Hellaswag: Can a machine really finish your sentence?
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Adversarial examples: Opportunities and challenges
Jiliang Zhang and Chen Li. 2019 · 2019
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Open knowledge enrichment for long-tail entities
Ermei Cao, Difeng Wang, Jiacheng Huang, and Wei Hu. 2020 · 2020
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Uncommonsense: Informative negative knowledge about everyday concepts
Hiba Arnaout, Simon Razniewski, Gerhard Weikum, and Jeff Z Pan. 2022 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Benchmarking long-tail generalization with likelihood splits
Ameya Godbole and Robin Jia. 2022 · 2022
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Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2022 · 2022
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Red teaming language models with language models
Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. 2022 · 2022
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Say what you mean! large language models speak too positively about negative commonsense knowledge
Jiangjie Chen, Wei Shi, Ziquan Fu, Sijie Cheng, Lei Li, and Yanghua Xiao. 2023 · 2023
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Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, et al. 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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Large language models struggle to learn long-tail knowledge
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023 · 2023
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Yasaman Razeghi, Robert L Logan IV, Matt Gardner, and Sameer Singh. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Adversarial training for high-stakes reliability
Daniel Ziegler, Seraphina Nix, Lawrence Chan, Tim Bauman, Peter Schmidt-Nielsen, Tao Lin, Adam Scherlis, Noa Nabeshima, Benjamin Weinstein-Raun, Daniel de Haas, et al. 2022 · 2022
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Explore, establish, exploit: Red teaming language models from scratch
Stephen Casper, Jason Lin, Joe Kwon, Gatlen Culp, and Dylan Hadfield-Menell. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023a
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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. 2023b
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R Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, and Thomas L Griffiths. 2023 · 2023
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Gpt-4 technical report
R OpenAI. 2023 · 2023
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Less likely brainstorming: Using language models to generate alternative hypotheses
Liyan Tang, Yifan Peng, Yanshan Wang, Ying Ding, Greg Durrett, and Justin F Rousseau. 2023 · 2023
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The dawn after the dark: An empirical study on factuality hallucination in large language models
Junyi Li, Jie Chen, Ruiyang Ren, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2024 · 2024
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Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data
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