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Large Language Models (LLMs) frequently lack domain-specific knowledge and even fine-tuned models tend to hallucinate.
Bender, E.M., Gebru, T., McMillan-Major, A., Shmitchell, S.: On the dangers of stochastic parrots: Can language models be too big? In: Proc. of FaccT 2021. p. 610–623 (2021). https://doi.org/10.1145/3442188.3445922
2021
Earlier work this paper cites.
Shakeel, D., Jain, N.: Fake news detection and fact verification using knowledge graphs and machine learning. ResearchGate preprint 10
2021
Earlier work this paper cites.
Vedula, N., Parthasarathy, S.: FACE-KEG: fact checking explained using knowledge graphs. In: Prof. of ACM WSDM 2021. pp. 526–534. ACM (2021). https://doi.org/10.1145/3437963.3441828
2021
Earlier work this paper cites.
Yasunaga, M., Ren, H., Bosselut, A., Liang, P., Leskovec, J.: Qa-gnn: Reasoning with language models and knowledge graphs for question answering. In: Proc. of the NAACL 2021. pp. 535–546 (2021)
2021
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2023
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2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
Mountantonakis, M., Tzitzikas, Y.: Using multiple RDF knowledge graphs for enriching ChatGPT responses. In: Prof. of the ECML PKDD 2023 Demo Track. pp. 324–329. Springer (2023). https://doi.org/10.1007/978-3-031-43430-3_24
2023
Cited alongside, same era.
Pal, A., Umapathi, L.K., Sankarasubbu, M.: Med-HALT: Medical domain hallucination test for large language models. In: Proc. of the 27th CoNLL. pp. 314–334 (2023). https://doi.org/10.18653/v1/2023.conll-1.21
2023
Cited alongside, same era.
Pan, J.Z., et al.: Large language models and knowledge graphs: Opportunities and challenges. TGDK 1
2023
Cited alongside, same era.
Agrawal, G., Kumarage, T., Alghamdi, Z., Liu, H.: Can knowledge graphs reduce hallucinations in llms?: A survey. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). pp. 3947–3960 (2024)
2024
Cited alongside, same era.
Kundu, A., Nguyen, U.T.: Automated fact checking using a knowledge graph-based model. In: Prof. of ICAIIC 2024. pp. 709–716 (2024). https://doi.org/10.1109/ICAIIC60209.2024.10463196
2024
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2024
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Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., Wu, X.: Unifying large language models and knowledge graphs: A roadmap. IEEE Trans. Knowl. Data Eng. 36
2024
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Wang, Y., Lipka, N., Rossi, R.A., Siu, A., Zhang, R., Derr, T.: Knowledge graph prompting for multi-document question answering. Proc. of AAAI 2024 (17), 19206–19214 (Mar 2024). https://doi.org/10.1609/aaai.v38i17.29889
2024
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Chia, Y.K., Hong, P., Bing, L., Poria, S.: InstructEval: Towards holistic evaluation of instruction-tuned large language models. In: Proc. of the First Workshop on the Scaling Behavior of Large Language Models (SCALE-LLM 2024). pp. 35–64 (2024)
2024
Cited alongside, same era.
Guan, X., Liu, Y., Lin, H., Lu, Y., He, B., Han, X., Sun, L.: Mitigating large language model hallucinations via autonomous knowledge graph-based retrofitting. In: Proc. of AAAI 2024. pp. 18126–18134 (2024)
2024
Cited alongside, same era.
Kambhampati, S.: Can large language models reason and plan? Annals of the New York Academy of Sciences 1534
2024
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2024
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Zhuang, H., Qin, Z., Hui, K., Wu, J., Yan, L., Wang, X., Bendersky, M.: Beyond yes and no: Improving zero-shot llm rankers via scoring fine-grained relevance labels. In: Proc. of the NAACL 2024. pp. 358–370 (2024)
2024
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