Foundations of GenIR | Paper Citation Record

Observation a66084a9-73c4-4a0c-bc22-bc425e42c47d · outbound

This paper cites GPT-4 Technical Report.

Foundations of GenIR GPT-4 Technical Report

Reference 1

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Observation d1a95302-f577-4b6a-99e5-24bdbbf3919d · outbound

This paper cites A Survey of Large Language Models.

Foundations of GenIR A Survey of Large Language Models

Reference 2

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Observation 7d0a19cc-bb2f-44ec-a247-16f4b0d571f3 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Foundations of GenIR LLaMA: Open and Efficient Foundation Language Models

Reference 3

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This paper cites In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R.

Foundations of GenIR In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R

Reference 4

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This paper cites IEEE transactions on Signal Processing 45(11), 2673–2681 (1997).

Foundations of GenIR IEEE transactions on Signal Processing 45(11), 2673–2681 (1997)

Reference 5

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Observation 207257f2-5c1e-4d59-b9c5-94302ccae827 · outbound

This paper cites GLM-130B: An Open Bilingual Pre-trained Model.

Foundations of GenIR GLM-130B: An Open Bilingual Pre-trained Model

Reference 6

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Foundations of GenIR Unresolved cited work

Reference 7

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Observation cdf474d4-b11b-42d3-89ed-e68a38b506d9 · outbound

This paper cites In: NAACL-HLT (1), pp.

Foundations of GenIR In: NAACL-HLT (1), pp

Reference 8

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Foundations of GenIR Unresolved cited work

Reference 9

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Observation 21a28681-bcc6-4e1e-b3db-6c939df5ffc9 · outbound

This paper cites Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

Foundations of GenIR Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Reference 10

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This paper cites DOI: https://doi.

Foundations of GenIR DOI: https://doi

Reference 11

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Observation c4d16eb3-21b7-4a90-8541-228304bfa19e · outbound

This paper cites GPT-NeoX-20B: An Open-Source Autoregressive Language Model.

Foundations of GenIR GPT-NeoX-20B: An Open-Source Autoregressive Language Model

Reference 12

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Observation 3af50368-935c-40df-bcc5-21ac506dd455 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Foundations of GenIR Generating Long Sequences with Sparse Transformers

Reference 13

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Observation 84b656ed-37a5-4a0f-b9e8-b58371eedce0 · outbound

This paper cites Reformer: The Efficient Transformer.

Foundations of GenIR Reformer: The Efficient Transformer

Reference 14

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Observation 96f68a58-24d1-4c94-af5b-10453ffedaf0 · outbound

This paper cites Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention.

Foundations of GenIR Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Reference 15

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Observation 91656d45-ac62-46f5-b49f-1020c31661bd · outbound

This paper cites Improving Neural Language Models with a Continuous Cache.

Foundations of GenIR Improving Neural Language Models with a Continuous Cache

Reference 16

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Observation 69d5a45c-cc4e-4608-8d1d-9a03a6ee5512 · outbound

This paper cites arXiv (2020).

Foundations of GenIR arXiv (2020)

Reference 17

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Observation 4005c333-9cd1-4706-b7ce-7470dec18b08 · outbound

This paper cites Fast Transformer Decoding: One Write-Head is All You Need.

Foundations of GenIR Fast Transformer Decoding: One Write-Head is All You Need

Reference 18

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This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

Foundations of GenIR GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 19

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Foundations of GenIR Unresolved cited work

Reference 20

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Observation 60664f72-0238-4d7d-b8e5-30959c172b1e · outbound

This paper cites In: International Conference on Machine Learning, pp.

Foundations of GenIR In: International Conference on Machine Learning, pp

Reference 21

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Observation 09a98b2f-d3bb-43b6-83c9-b8ff0211d868 · outbound

This paper cites Advances in Neural Information Processing Systems 34, 19822– 19835 (2021).

Foundations of GenIR Advances in Neural Information Processing Systems 34, 19822– 19835 (2021)

Reference 22

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Observation ac08013b-12e0-45fb-8435-f4471b0b3ae5 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence (2024).

Foundations of GenIR IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

Reference 23

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Observation 1d3a710f-f639-4a7c-ba4d-b890d71d38ba · outbound

This paper cites Scaling Laws for Neural Language Models.

Foundations of GenIR Scaling Laws for Neural Language Models

Reference 24

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Observation 33e61d43-a6bb-48c0-b924-73d3a269abb5 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Foundations of GenIR Training Compute-Optimal Large Language Models

Reference 25

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Observation 8fc469a8-3efa-48fb-9b30-85ab60163464 · outbound

This paper cites Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance.

Foundations of GenIR Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance

Reference 26

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Observation daad05c8-594c-44db-80c1-e730ddb80296 · outbound

This paper cites Scaling Laws for Autoregressive Generative Modeling.

Foundations of GenIR Scaling Laws for Autoregressive Generative Modeling

Reference 27

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Observation b335f1ac-5630-4d74-ade4-2497e46f55d3 · outbound

This paper cites Emergent Abilities of Large Language Models.

Foundations of GenIR Emergent Abilities of Large Language Models

Reference 29

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Observation 1016a9c1-1dfb-4190-b32a-2ad2ae58bb95 · outbound

This paper cites Understanding Emergent Abilities of Language Models from the Loss Perspective.

Foundations of GenIR Understanding Emergent Abilities of Language Models from the Loss Perspective

Reference 30

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Observation 1999e124-e187-477c-956c-ccd75d0e80d3 · outbound

This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

Foundations of GenIR Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 31

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Observation 11295a71-2381-417a-b900-8b2d4434eeb0 · outbound

This paper cites NIPS ’23, pp.

Foundations of GenIR NIPS ’23, pp

Reference 32

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Observation ebcf4630-1db2-46a4-b850-138f27146c93 · outbound

This paper cites Inverse Scaling: When Bigger Isn't Better.

Foundations of GenIR Inverse Scaling: When Bigger Isn't Better

Reference 33

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Observation 731ac2d3-ff3d-4849-bbed-b1727d88cc57 · outbound

This paper cites Bigger is not Always Better: Scaling Properties of Latent Diffusion Models.

Foundations of GenIR Bigger is not Always Better: Scaling Properties of Latent Diffusion Models

Reference 34

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Observation 93dfcfb5-f55c-4792-9778-0082d98916ca · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

Foundations of GenIR MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 35

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Observation d7837f91-02cb-42a2-866d-1ecae2b160fc · outbound

This paper cites OpenAI blog 1(8), 9 (2019).

Foundations of GenIR OpenAI blog 1(8), 9 (2019)

Reference 36

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This paper cites OPT: Open Pre-trained Transformer Language Models.

Foundations of GenIR OPT: Open Pre-trained Transformer Language Models

Reference 37

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Observation 9c5b579d-2734-4e70-ba89-1d859efae8ca · outbound

This paper cites Journal of Machine Learning Research 24(240), 1–113 (2023).

Foundations of GenIR Journal of Machine Learning Research 24(240), 1–113 (2023)

Reference 38

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This paper cites Textbooks Are All You Need.

Foundations of GenIR Textbooks Are All You Need

Reference 39

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This paper cites Baichuan 2: Open Large-scale Language Models.

Foundations of GenIR Baichuan 2: Open Large-scale Language Models

Reference 40

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This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

Foundations of GenIR DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 41

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This paper cites Scaling Instruction-Finetuned Language Models.

Foundations of GenIR Scaling Instruction-Finetuned Language Models

Reference 42

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This paper cites Advances in neural information processing systems 35, 27730–27744 (2022).

Foundations of GenIR Advances in neural information processing systems 35, 27730–27744 (2022)

Reference 43

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Foundations of GenIR Unresolved cited work

Reference 44

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This paper cites In: Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S.

Foundations of GenIR In: Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S

Reference 45

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This paper cites Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study.

Foundations of GenIR Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study

Reference 46

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This paper cites ACM Computing Surveys 55(9), 1–35 (2023).

Foundations of GenIR ACM Computing Surveys 55(9), 1–35 (2023)

Reference 47

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This paper cites Advances in neural information processing systems 35, 24824–24837 (2022).

Foundations of GenIR Advances in neural information processing systems 35, 24824–24837 (2022)

Reference 48

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Foundations of GenIR In: Proceedings of the 37th International Conference on Neural Information Processing Systems

Reference 49

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This paper cites In: 11th International Conference on Learning Representations, ICLR 2023, pp.

Foundations of GenIR In: 11th International Conference on Learning Representations, ICLR 2023, pp

Reference 50

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Foundations of GenIR Large Language Models Are Human-Level Prompt Engineers

Reference 51

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Foundations of GenIR Large Language Models as Optimizers

Reference 52

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Foundations of GenIR In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Infor- mation Retrieval

Reference 53

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This paper cites In: Ku, L.-W., Martins, A., Srikumar, V.

Foundations of GenIR In: Ku, L.-W., Martins, A., Srikumar, V

Reference 54

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This paper cites Advances in neural information processing systems 32 (2019) 26.

Foundations of GenIR Advances in neural information processing systems 32 (2019) 26

Reference 55

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Foundations of GenIR In: European Conference on Computer Vision, pp

Reference 56

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This paper cites Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers.

Foundations of GenIR Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers

Reference 57

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This paper cites In: International Conference on Machine Learning, pp.

Foundations of GenIR In: International Conference on Machine Learning, pp

Reference 58

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This paper cites Advances in neural information processing systems 35, 23716–23736 (2022).

Foundations of GenIR Advances in neural information processing systems 35, 23716–23736 (2022)

Reference 59

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Foundations of GenIR CogVLM: Visual Expert for Pretrained Language Models

Reference 60

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This paper cites In: International Conference on Machine Learning, pp.

Foundations of GenIR In: International Conference on Machine Learning, pp

Reference 61

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This paper cites In: International Conference on Machine Learning, pp.

Foundations of GenIR In: International Conference on Machine Learning, pp

Reference 62

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This paper cites Advances in neural information processing systems 34, 9694–9705 (2021).

Foundations of GenIR Advances in neural information processing systems 34, 9694–9705 (2021)

Reference 63

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This paper cites In: International Conference on Machine Learning, pp.

Foundations of GenIR In: International Conference on Machine Learning, pp

Reference 64

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This paper cites RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback.

Foundations of GenIR RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback

Reference 65

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Foundations of GenIR BEiT: BERT Pre-Training of Image Transformers

Reference 66

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This paper cites In: International Conference on Machine Learning, pp.

Foundations of GenIR In: International Conference on Machine Learning, pp

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Foundations of GenIR Natural Language Processing Journal 7, 100065 (2024)

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Foundations of GenIR Unsupervised Domain Clusters in Pretrained Language Models

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Foundations of GenIR Advances in Neural Information Processing Systems 33, 9459–9474 (2020)

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Foundations of GenIR Retrieval-Augmented Generation for AI-Generated Content: A Survey

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Foundations of GenIR In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts), pp

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Foundations of GenIR Query Rewriting for Retrieval-Augmented Large Language Models

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Foundations of GenIR Frontiers of Computer Science 18(6), 186345 (2024)

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Foundations of GenIR Foundations and Trends® in Information Retrieval 3(4), 333–389 (2009)

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Foundations of GenIR RaFe: Ranking Feedback Improves Query Rewriting for RAG

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Foundations of GenIR RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation

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Foundations of GenIR Long-context LLMs Struggle with Long In-context Learning

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Foundations of GenIR Transactions of the Association for Computational Linguistics 12, 157–173 (2024)

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