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Incorporating factual knowledge in knowledge graph is regarded as a promising approach for mitigating the hallucination of large language models (LLMs).
Sticking to the facts: Confident decoding for faithful data-to-text generation
Tian, R.; Narayan, S.; Sellam, T.; and Parikh, A. P. 2019 · 1910
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Improved Natural Language Generation via Loss Truncation
Kang, D.; and Hashimoto, T. 2020 · 2004
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Wikidata: a free collaborative knowledgebase
Vrandečić, D.; and Krötzsch, M. 2014 · 2014
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Large-scale simple question answering with memory networks
Bordes, A.; Usunier, N.; Chopra, S.; and Weston, J. 2015 · 2015
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Annotating documents with relevant wikipedia concepts
Brank, J.; Leban, G.; and Grobelnik, M. 2017 · 2017
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Yang, Z.; Qi, P.; Zhang, S.; Bengio, Y.; Cohen, W. W.; Salakhutdinov, R.; and Manning, C. D. 2018 · 2018
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ERNIE: Enhanced Language Representation with Informative Entities
Zhang, Z.; Han, X.; Liu, Z.; Jiang, X.; Sun, M.; and Liu, Q. 2019 · 2019
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spaCy: Industrial-strength Natural Language Processing in Python
Honnibal, M.; Montani, I.; Van Landeghem, S.; and Boyd, A. 2020 · 2020
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Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View
Liu, T.; Zheng, X.; Chang, B.; and Sui, Z. 2021 · 2021
Earlier work this paper cites.
Calibrate before use: Improving few-shot performance of language models
Zhao, Z.; Wallace, E.; Feng, S.; Klein, D.; and Singh, S. 2021 · 2021
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KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base
Cao, S.; Shi, J.; Pan, L.; Nie, L.; Xiang, Y.; Hou, L.; Li, J.; He, B.; and Zhang, H. 2022 · 2022
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LangChain
Chase, H. 2022 · 2022
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RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding
Ji, Z.; Liu, Z.; Lee, N.; Yu, T.; Wilie, B.; Zeng, M.; and Fung, P. 2022 · 2022
Cited alongside, same era.
Deduplicating Training Data Makes Language Models Better
Lee, K.; Ippolito, D.; Nystrom, A.; Zhang, C.; Eck, D.; Callison-Burch, C.; and Carlini, N. 2022 · 2022
Cited alongside, same era.
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity
Lu, Y.; Bartolo, M.; Moore, A.; Riedel, S.; and Stenetorp, P. 2022 · 2022
Cited alongside, same era.
Introducing ChatGPT
OpenAI. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; et al. 2022 · 2022
Cited alongside, same era.
Chern, I.-C.; Chern, S.; Chen, S.; Yuan, W.; Feng, K.; Zhou, C.; He, J.; Neubig, G.; and Liu, P. 2023 · 2023
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Main Page — Wikimedia Commons, the free media repository
Commons, W. 2023 · 2023
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Rarr: Researching and revising what language models say, using language models
Gao, L.; Dai, Z.; Pasupat, P.; Chen, A.; Chaganty, A. T.; Fan, Y.; Zhao, V.; Lao, N.; Lee, H.; Juan, D.-C.; et al. 2023 · 2023
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Critic: Large language models can self-correct with tool-interactive critiquing
Gou, Z.; Shao, Z.; Gong, Y.; Shen, Y.; Yang, Y.; Duan, N.; and Chen, W. 2023 · 2023
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Gunasekar, S.; Zhang, Y.; Aneja, J.; Mendes, C. C. T.; Del Giorno, A.; Gopi, S.; Javaheripi, M.; Kauffmann, P.; de Rosa, G.; Saarikivi, O.; et al. 2023 · 2023
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Sen, P.; Aji, A. F.; and Saffari, A. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Xia, F.; Chi, E.; Le, Q. V.; Zhou, D.; et al. 2022 · 2022
Cited alongside, same era.
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding
Zhang, T.; Wang, C.; Hu, N.; Qiu, M.; Tang, C.; He, X.; and Huang, J. 2022 · 2022
Cited alongside, same era.
KITLM: Domain-Specific Knowledge InTegration into Language Models for Question Answering
Agarwal, A.; Gawade, S.; Azad, A. P.; and Bhattacharyya, P. 2023 · 2023
Cited alongside, same era.
Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
Baek, J.; Aji, A. F.; and Saffari, A. 2023 · 2023
Cited alongside, same era.
Pythia: A suite for analyzing large language models across training and scaling
Biderman, S.; Schoelkopf, H.; Anthony, Q. G.; Bradley, H.; O’Brien, K.; Hallahan, E.; Khan, M. A.; Purohit, S.; Prashanth, U. S.; Raff, E.; et al. 2023 · 2023
Cited alongside, same era.
PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions
Chen, A.; Pasupat, P.; Singh, S.; Lee, H.; and Guu, K. 2023 · 2023
Cited alongside, same era.
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Survey of hallucination in natural language generation
Ji, Z.; Lee, N.; Frieske, R.; Yu, T.; Su, D.; Xu, Y.; Ishii, E.; Bang, Y. J.; Madotto, A.; and Fung, P. 2023 · 2023
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StructGPT: A general framework for Large Language Model to Reason on Structured Data
Jiang, J.; Zhou, K.; Dong, Z.; Ye, K.; Zhao, W. X.; and Wen, J.-R. 2023 · 2023
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Chameleon: Plug-and-play compositional reasoning with large language models
Lu, P.; Peng, B.; Cheng, H.; Galley, M.; Chang, K.-W.; Wu, Y. N.; Zhu, S.-C.; and Gao, J. 2023 · 2023
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Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Manakul, P.; Liusie, A.; and Gales, M. J. 2023 · 2023
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Wikimedia movement — Meta, discussion about Wikimedia projects
Meta. 2023 · 2023
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LLaMA: Open and Efficient Foundation Language Models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; Rodriguez, A.; Joulin, A.; Grave, E.; and Lample, G. 2023 · 2023
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Can We Edit Factual Knowledge by In-Context Learning?
Zheng, C.; Li, L.; Dong, Q.; Fan, Y.; Wu, Z.; Xu, J.; and Chang, B. 2023 · 2023
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