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In order to thrive in hostile and ever-changing natural environments, mammalian brains evolved to store large amounts of knowledge about the world and continually integrate new information while avoiding catastrophic forgetting.
Huggingface’s transformers: State-of-the-art natural language processing
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. L. Scao, S. Gugger, M. Drame, Q. Lhoest, and A. M. Rush · 1910
Earlier work this paper cites.
The hippocampal memory indexing theory
T. J. Teyler and P. Discenna · 1986
Earlier work this paper cites.
A cortical–hippocampal system for declarative memory
H. Eichenbaum · 2000
Earlier work this paper cites.
Topic-sensitive pagerank
T. H. Haveliwala · 2002
Earlier work this paper cites.
The igraph software package for complex network research
G. Csárdi and T. Nepusz · 2006
Earlier work this paper cites.
Open information extraction from the web
M. Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni · 2007
Earlier work this paper cites.
Google and the mind
T. L. Griffiths, M. Steyvers, and A. J. Firl · 2007
Earlier work this paper cites.
The hippocampal indexing theory and episodic memory: Updating the index
T. J. Teyler and J. W. Rudy · 2007
Earlier work this paper cites.
Leveraging linguistic structure for open domain information extraction
G. Angeli, M. J. Johnson Premkumar, and C. D. Manning · 2015
Earlier work this paper cites.
Elasticsearch: The definitive guide
C. Gormley and Z. J. Tong · 2015
Earlier work this paper cites.
HotpotQA: A dataset for diverse, explainable multi-hop question answering
Z. Yang, P. Qi, S. Zhang, Y. Bengio, W. W. Cohen, R. Salakhutdinov, and C. D. Manning · 2018
Earlier work this paper cites.
CaRB: A crowdsourced benchmark for open IE
S. Bhardwaj, S. Aggarwal, and Mausam · 2019
Earlier work this paper cites.
COMET: Commonsense transformers for automatic knowledge graph construction
A. Bosselut, H. Rashkin, M. Sap, C. Malaviya, A. Celikyilmaz, and Y. Choi · 2019
Earlier work this paper cites.
Multi-step entity-centric information retrieval for multi-hop question answering
R. Das, A. Godbole, D. Kavarthapu, Z. Gong, A. Singhal, M. Yu, X. Guo, T. Gao, H. Zamani, M. Zaheer, and A. McCallum · 2019
Earlier work this paper cites.
Cognitive graph for multi-hop reading comprehension at scale
M. Ding, C. Zhou, Q. Chen, H. Yang, and J. Tang · 2019
Earlier work this paper cites.
Revealing the importance of semantic retrieval for machine reading at scale
Y. Nie, S. Wang, and M. Bansal · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Z. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
Earlier work this paper cites.
Language models as knowledge bases?
F. Petroni, T. Rocktäschel, S. Riedel, P. Lewis, A. Bakhtin, Y. Wu, and A. Miller · 2019
Earlier work this paper cites.
Dynamically fused graph network for multi-hop reasoning
L. Qiu, Y. Xiao, Y. Qu, H. Zhou, L. Li, W. Zhang, and Y. Yu · 2019
Earlier work this paper cites.
Learning to retrieve reasoning paths over wikipedia graph for question answering
A. Asai, K. Hashimoto, H. Hajishirzi, R. Socher, and C. Xiong · 2020
Earlier work this paper cites.
Hierarchical graph network for multi-hop question answering
Y. Fang, S. Sun, Z. Gan, R. Pillai, S. Wang, and J. Liu · 2020
Earlier work this paper cites.
Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps
X. Ho, A.-K. Duong Nguyen, S. Sugawara, and A. Aizawa · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive NLP tasks
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, S. Riedel, and D. Kiela · 2020
Earlier work this paper cites.
Editing factual knowledge in language models
N. De Cao, W. Aziz, and I. Titov · 2021
Earlier work this paper cites.
REBEL: Relation extraction by end-to-end language generation
P.-L. Huguet Cabot and R. Navigli · 2021
Earlier work this paper cites.
Unsupervised dense information retrieval with contrastive learning, 2021
G. Izacard, M. Caron, L. Hosseini, S. Riedel, P. Bojanowski, A. Joulin, and E. Grave · 2021
Earlier work this paper cites.
Hopretriever: Retrieve hops over wikipedia to answer complex questions
S. Li, X. Li, L. Shang, X. Jiang, Q. Liu, C. Sun, Z. Ji, and B. Liu · 2021
Earlier work this paper cites.
E. Mitchell, C. Lin, A. Bosselut, C. Finn, and C. D. Manning · 2021
Earlier work this paper cites.
Answering complex open-domain questions with multi-hop dense retrieval
W. Xiong, X. Li, S. Iyer, J. Du, P. Lewis, W. Y. Wang, Y. Mehdad, S. Yih, S. Riedel, D. Kiela, and B. Oguz · 2021
Earlier work this paper cites.
Adaptive information seeking for open-domain question answering
Y. Zhu, L. Pang, Y. Lan, H. Shen, and X. Cheng · 2021
Earlier work this paper cites.
A review on language models as knowledge bases
B. AlKhamissi, M. Li, A. Celikyilmaz, M. T. Diab, and M. Ghazvininejad · 2022
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Few-shot learning with retrieval augmented language models
G. Izacard, P. Lewis, M. Lomeli, L. Hosseini, F. Petroni, T. Schick, J. A. Yu, A. Joulin, S. Riedel, and E. Grave · 2022
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Locating and editing factual associations in gpt
K. Meng, D. Bau, A. Andonian, and Y. Belinkov · 2022
Cited alongside, same era.
Memory-based model editing at scale
E. Mitchell, C. Lin, A. Bosselut, C. D. Manning, and C. Finn · 2022
Cited alongside, same era.
A survey of machine unlearning
T. T. Nguyen, T. T. Huynh, P. L. Nguyen, A. W.-C. Liew, H. Yin, and Q. V. H. Nguyen · 2022
Mindmap: Knowledge graph prompting sparks graph of thoughts in large language models
Y. Wen, Z. Wang, and J. Sun · 2023
Later among the works it cites.
ReAct: Synergizing reasoning and acting in language models
S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao · 2023
Later among the works it cites.
Answering questions by meta-reasoning over multiple chains of thought
O. Yoran, T. Wolfson, B. Bogin, U. Katz, D. Deutch, and J. Berant · 2023
Later among the works it cites.
Generate rather than retrieve: Large language models are strong context generators
W. Yu, D. Iter, S. Wang, Y. Xu, M. Ju, S. Sanyal, C. Zhu, M. Zeng, and M. Jiang · 2023
Later among the works it cites.
Aligning instruction tasks unlocks large language models as zero-shot relation extractors
K. Zhang, B. Jimenez Gutierrez, and Y. Su · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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Large dual encoders are generalizable retrievers
J. Ni, C. Qu, J. Lu, Z. Dai, G. Hernandez Abrego, J. Ma, V. Zhao, Y. Luan, K. Hall, M.-W. Chang, and Y. Yang · 2022
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ColBERTv2: Effective and efficient retrieval via lightweight late interaction
K. Santhanam, O. Khattab, J. Saad-Falcon, C. Potts, and M. Zaharia · 2022
Cited alongside, same era.
MuSiQue: Multihop questions via single-hop question composition
H. Trivedi, N. Balasubramanian, T. Khot, and A. Sabharwal · 2022
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Chain of thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, brian ichter, F. Xia, E. H. Chi, Q. V. Le, and D. Zhou · 2022
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Symbolic knowledge distillation: from general language models to commonsense models
P. West, C. Bhagavatula, J. Hessel, J. Hwang, L. Jiang, R. Le Bras, X. Lu, S. Welleck, and Y. Choi · 2022
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Deep bidirectional language-knowledge graph pretraining
M. Yasunaga, A. Bosselut, H. Ren, X. Zhang, C. D. Manning, P. Liang, and J. Leskovec · 2022
Cited alongside, same era.
A survey on neural open information extraction: Current status and future directions
S. Zhou, B. Yu, A. Sun, C. Long, J. Li, and J. Sun · 2022
Cited alongside, same era.
Mquake: Assessing knowledge editing in language models via multi-hop questions
Z. Zhong, Z. Wu, C. D. Manning, C. Potts, and D. Chen · 2023
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Llama 3 model card
AI@Meta · 2024
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Journey to the center of the knowledge neurons: Discoveries of language-independent knowledge neurons and degenerate knowledge neurons
Y. Chen, P. Cao, Y. Chen, K. Liu, and J. Zhao · 2024
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Longrope: Extending llm context window beyond 2 million tokens
Y. Ding, L. L. Zhang, C. Zhang, Y. Xu, N. Shang, J. Xu, F. Yang, and M. Yang · 2024
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From local to global: A graph rag approach to query-focused summarization
D. Edge, H. Trinh, N. Cheng, J. Bradley, A. Chao, A. Mody, S. Truitt, and J. Larson · 2024
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Challenges in deploying long-context transformers: A theoretical peak performance analysis, 2024
Y. Fu · 2024
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Data engineering for scaling language models to 128k context, 2024
Y. Fu, R. Panda, X. Niu, X. Yue, H. Hajishirzi, Y. Kim, and H. Peng · 2024
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Model Editing Can Hurt General Abilities of Large Language Models, 2024
J.-C. Gu, H.-X. Xu, J.-Y. Ma, P. Lu, Z.-H. Ling, K.-W. Chang, and N. Peng · 2024
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Language models represent space and time
W. Gurnee and M. Tegmark · 2024
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CAMELot: Towards large language models with training-free consolidated associative memory
Z. He, L. Karlinsky, D. Kim, J. McAuley, D. Krotov, and R. Feris · 2024
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Can large language models reason and plan?
S. Kambhampati · 2024
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Same task, more tokens: the impact of input length on the reasoning performance of large language models, 2024
M. Levy, A. Jacoby, and Y. Goldberg · 2024
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ERA-CoT: Improving chain-of-thought through entity relationship analysis
Y. Liu, X. Peng, T. Du, J. Yin, W. Liu, and X. Zhang · 2024
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Reasoning on graphs: Faithful and interpretable large language model reasoning
L. LUO, Y.-F. Li, R. Haf, and S. Pan · 2024
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GPT-3.5 Turbo, 2024
OpenAI · 2024
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Unifying large language models and knowledge graphs: A roadmap
S. Pan, L. Luo, Y. Wang, C. Chen, J. Wang, and X. Wu · 2024
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Graph elicitation for guiding multi-step reasoning in large language models, 2024
J. Park, A. Patel, O. Z. Khan, H. J. Kim, and J.-K. Kim · 2024
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Memoria: Resolving fateful forgetting problem through human-inspired memory architecture
S. Park and J. Bak · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M. Reid, N. Savinov, D. Teplyashin, D. Lepikhin, T. Lillicrap, J.-b. Alayrac, R. Soricut, A. Lazaridou, O. Firat, J. Schrittwieser, et al · 2024
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RAPTOR: recursive abstractive processing for tree-organized retrieval
P. Sarthi, S. Abdullah, A. Tuli, S. Khanna, A. Goldie, and C. D. Manning · 2024
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Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph
J. Sun, C. Xu, L. Tang, S. Wang, C. Lin, Y. Gong, L. Ni, H.-Y. Shum, and J. Guo · 2024
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MEMORYLLM: Towards self-updatable large language models
Y. Wang, Y. Gao, X. Chen, H. Jiang, S. Li, J. Yang, Q. Yin, Z. Li, X. Li, B. Yin, J. Shang, and J. Mcauley · 2024
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Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts
J. Xie, K. Zhang, J. Chen, R. Lou, and Y. Su · 2024
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Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval
S. E. Robertson and S. Walker · 2099
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