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Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance.
Abductive reasoning in logistics research
Gyöngyi Kovács and Karen M Spens · 2005
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Abductive reasoning
Douglas Walton · 2014
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Language models are few-shot learners
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The turking test: Can language models understand instructions?
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How can we know what language models know?
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh · 2020
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Learning from task descriptions
Orion Weller, Nicholas Lourie, Matt Gardner, and Matthew E Peters · 2020
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Graph infomax adversarial learning for treatment effect estimation with networked observational data
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Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, and Carrie J Cai · 2022
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
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Gpt-4 technical report, 2023
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Large language models are in-context semantic reasoners rather than symbolic reasoners
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Llama: Open and efficient foundation language models
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Enhancing recommender systems with large language model reasoning graphs
Yan Wang, Zhixuan Chu, Xin Ouyang, Simeng Wang, Hongyan Hao, Yue Shen, Jinjie Gu, Siqiao Xue, James Y Zhang, Qing Cui, et al · 2023
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What makes good in-context examples for gpt- 3 3 ?
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig
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Prompt-augmented temporal point process for streaming event sequence
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Weaverbird: Empowering financial decision-making withlarge language model, knowledge base, and search engine
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