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This survey provides an in-depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge.
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DIALOGPT : Large-scale generative pre-training for conversational response generation
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Explanations for CommonsenseQA: New Dataset and Models
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Cross-lingual evidence improves monolingual fake news detection
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Neural path hunter: Reducing hallucination in dialogue systems via path grounding
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Measuring and improving consistency in pretrained language models
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Analyzing the forgetting problem in pretrain-finetuning of open-domain dialogue response models
Tianxing He, Jun Liu, Kyunghyun Cho, Myle Ott, Bing Liu, James Glass, and Fuchun Peng. 2021 · 2021
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Wikicontradiction: Detecting self-contradiction articles on wikipedia
Cheng Hsu, Cheng-Te Li, Diego Saez-Trumper, and Yi-Zhan Hsu. 2021 · 2021
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Towards continual knowledge learning of language models
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Mind the gap: Assessing temporal generalization in neural language models
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Situatedqa: Incorporating extra-linguistic contexts into qa
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Measuring causal effects of data statistics on language model’sfactual’predictions
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Large language models struggle to learn long-tail knowledge
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Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
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Dynamic benchmarking of masked language models on temporal concept drift with multiple views
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Addressing the harms of ai-generated inauthentic content
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Large language models challenge the future of higher education
Silvia Milano, Joshua A McGrane, and Sabina Leonelli. 2023 · 2023
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Self-contradictory hallucinations of large language models: Evaluation, detection and mitigation
Niels Mündler, Jingxuan He, Slobodan Jenko, and Martin Vechev. 2023 · 2023
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A comprehensive overview of large language models
Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Nick Barnes, and Ajmal Mian. 2023 · 2023
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Separating form and meaning: Using self-consistency to quantify task understanding across multiple senses
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Can lms learn new entities from descriptions? challenges in propagating injected knowledge
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Chatgpt
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Emptying the ocean with a spoon: Should we edit models?
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Cross-lingual consistency of factual knowledge in multilingual language models
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" merge conflicts!" exploring the impacts of external distractors to parametric knowledge graphs
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Predicting question-answering performance of large language models through semantic consistency
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Semantic consistency for assuring reliability of large language models
Harsh Raj, Vipul Gupta, Domenic Rosati, and Subhabrata Majumdar. 2023 · 2023
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023 · 2023
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Averitec: A dataset for real-world claim verification with evidence from the web
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Towards understanding sycophancy in language models
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A comprehensive evaluation of large language models on legal judgment prediction
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What large models cost you – there is no free ai lunch
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Evaluating the social impact of generative ai systems in systems and society
Irene Solaiman, Zeerak Talat, William Agnew, Lama Ahmad, Dylan Baker, Su Lin Blodgett, Hal Daumé III, Jesse Dodge, Ellie Evans, Sara Hooker, et al. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
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Freshllms: Refreshing large language models with search engine augmentation
Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, et al. 2023 · 2023
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Simple synthetic data reduces sycophancy in large language models
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Sociotechnical safety evaluation of generative ai systems
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Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation
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Video-llama: An instruction-tuned audio-visual language model for video understanding
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Mitigating temporal misalignment by discarding outdated facts
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Mquake: Assessing knowledge editing in language models via multi-hop questions
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Synthetic lies: Understanding ai-generated misinformation and evaluating algorithmic and human solutions
Jiawei Zhou, Yixuan Zhang, Qianni Luo, Andrea G Parker, and Munmun De Choudhury. 2023c · 2023
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Toolqa: A dataset for llm question answering with external tools
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Summing up the facts: Additive mechanisms behind factual recall in llms
Bilal Chughtai, Alan Cooney, and Neel Nanda. 2024 · 2024
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Evaluating robustness of generative search engine on adversarial factual questions
Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip S. Yu, and Zhijiang Guo. 2024 · 2024
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Better call gpt, comparing large language models against lawyers
Lauren Martin, Nick Whitehouse, Stephanie Yiu, Lizzie Catterson, and Rivindu Perera. 2024 · 2024
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OpenAI. 2024 · 2024
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Hexiang Tan, Fei Sun, Wanli Yang, Yuanzhuo Wang, Qi Cao, and Xueqi Cheng. 2024 · 2024
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Comparing gpt-4 and open-source language models in misinformation mitigation
Tyler Vergho, Jean-Francois Godbout, Reihaneh Rabbany, and Kellin Pelrine. 2024 · 2024
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What evidence do language models find convincing?
Alexander Wan, Eric Wallace, and Dan Klein. 2024 · 2024
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A comprehensive study of multilingual confidence estimation on large language models
Boyang Xue, Hongru Wang, Weichao Wang, Rui Wang, Sheng Wang, Zeming Liu, and Kam-Fai Wong. 2024 · 2024
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Exploiting Abstract Meaning Representation for open-domain question answering
Cunxiang Wang, Zhikun Xu, Qipeng Guo, Xiangkun Hu, Xuefeng Bai, Zheng Zhang, and Yue Zhang. 2023b · 2096
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