uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?

Language models are few-shot learners

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Semeval-2024 task 9: Brainteaser: A novel task defying common sense

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Large language models are zero-shot reasoners

Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022

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Do prompt-based models really understand the meaning of their prompts?

Albert Webson and Ellie Pavlick. 2022 · 2022

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Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022 · 2022

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Automatic chain of thought prompting in large language models

Original

Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022 · 2022

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LM-CPPF: Paraphrasing-guided data augmentation for contrastive prompt-based few-shot fine-tuning

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Active prompting with chain-of-thought for large language models

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Shizhe Diao, Pengcheng Wang, Yong Lin, and Tong Zhang. 2023 · 2023

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Promptbreeder: Self-referential self-improvement via prompt evolution

Original

BRAINTEASER: Lateral thinking puzzles for large language models

Yifan Jiang, Filip Ilievski, Kaixin Ma, and Zhivar Sourati. 2023c · 2023

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Zephyr: Direct distillation of lm alignment

Original

Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, et al. 2023 · 2023

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C-pack: Packaged resources to advance general chinese embedding

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Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff. 2023 · 2023

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Meta-cot: Generalizable chain-of-thought prompting in mixed-task scenarios with large language models

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Anni Zou, Zhuosheng Zhang, Hai Zhao, and Xiangru Tang. 2023 · 2023

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Rag-fusion: a new take on retrieval-augmented generation