Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? | Paper Citation Record

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This paper cites Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) , year=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) , year=

Reference 1

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This paper cites Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics , pages=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics , pages=

Reference 2

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2025 , eprint=

Reference 3

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This paper cites VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks

Reference 4

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2025 , eprint=

Reference 5

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This paper cites VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents

Reference 6

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This paper cites BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval

Reference 7

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2024 , address=

Reference 8

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Observation d61ddcb1-e4cc-498d-88a0-eae3e4089fee · outbound

This paper cites Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

Reference 9

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This paper cites Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking

Reference 10

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Qwen3 Technical Report

Reference 11

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Qwen3-VL Technical Report

Reference 12

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2511.00405 , archivePrefix=

Reference 13

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2602.13823 , archivePrefix=

Reference 14

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This paper cites What you can cram into a single.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? What you can cram into a single

Reference 15

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Reference 16

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Reference 17

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This paper cites Transactions of the Association for Computational Linguistics , volume=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Transactions of the Association for Computational Linguistics , volume=

Reference 18

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This paper cites Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages=

Reference 19

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 20

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This paper cites Findings of the Association for Computational Linguistics: ACL 2023 , pages=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Findings of the Association for Computational Linguistics: ACL 2023 , pages=

Reference 21

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2025 , address=

Reference 22

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2025 , eprint=

Reference 23

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2026 , eprint=

Reference 24

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Proceedings of the First Workshop on Large Language Model Memorization (L2M2) , pages=

Reference 25

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Training

Reference 26

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 27

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2024 , eprint=

Reference 28

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This paper cites Advances in Neural Information Processing Systems , volume=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Advances in Neural Information Processing Systems , volume=

Reference 29

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token

Reference 30

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 31

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2023 , eprint=

Reference 32

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents

Reference 33

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2509.24704 , archivePrefix=

Reference 34

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2023 , address=

Reference 36

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Findings of the Association for Computational Linguistics: ACL 2024 , pages=

Reference 37

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2025 , eprint=

Reference 38

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This paper cites LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 39

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2024 , eprint=

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Reference 41

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2026 , eprint=

Reference 42

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Reference 43

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=

Reference 44

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Advances in Neural Information Processing Systems , volume=

Reference 45

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Learning Transferable Visual Models From Natural Language Supervision

Reference 46

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

Reference 47

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2024 , url=

Reference 48

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=

Reference 50

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2025 , url=

Reference 52

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Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

Reference 53

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 735a8cf2-57f3-4e9c-a3f0-108c3b5287e1 · outbound

This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T17:26:11.833434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f34c628c-af02-435c-a0e3-ba64a62f7f5f · outbound

This paper cites an unresolved cited work.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:26:12.629311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 79e3672e-ba22-492f-9cc2-e65722866235 · outbound

This paper cites an unresolved cited work.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Unresolved cited work

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T17:26:11.841114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dd0eda9-5e0e-4681-bfdd-4649f5e87b23 · outbound

This paper cites an unresolved cited work.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:26:12.610238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 106c122e-551d-4572-ba7d-4f1e56cd096e · outbound

This paper cites 2022 , address=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2022 , address=

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:26:12.597736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 877c1200-1088-404c-8e25-59c51994a809 · outbound

This paper cites an unresolved cited work.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Unresolved cited work

Reference 59

Resolution: parse uncertain; no resolver link, observed 2026-08-10T17:26:11.851836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6edebeb6-960f-4a1d-8278-055105ddce6c · outbound

This paper cites 2025 , address=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2025 , address=

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:26:12.576176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b00c98df-2240-4acb-9f83-0752b7689a61 · outbound

This paper cites 2002 , address=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2002 , address=

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:26:12.563064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c5fcfc5b-3fda-4b07-95a9-9c13ba348f95 · outbound

This paper cites Lawrence and Parikh, Devi , booktitle=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Lawrence and Parikh, Devi , booktitle=

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:26:12.550709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 49521f3e-89e4-405b-84a6-d3e1d6d71b29 · outbound

This paper cites Microsoft.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Microsoft

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:26:12.537867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 978fc593-a293-462e-a8ed-41e1336d453c · outbound

This paper cites Proceedings of the Sixth Workshop on Statistical Machine Translation , pages=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Proceedings of the Sixth Workshop on Statistical Machine Translation , pages=

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:26:12.523179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 929a9e97-e7d0-4d3d-b1c6-270ebc044393 · outbound

This paper cites 2004 , address=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? 2004 , address=

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-10T17:26:11.875039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 185aae7f-be0f-4726-960d-7da0107e1e6e · outbound

This paper cites Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:26:12.501617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation de6dcdb4-4bb6-4e7d-a7c0-1c0228e18b38 · outbound

This paper cites DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-10T17:26:11.883749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23c01663-730c-4f84-901e-25dcf7df9ed9 · outbound

This paper cites Implicit Inversion turns.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Implicit Inversion turns

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:26:12.488128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 40e85c06-1a8c-4b62-aa53-4485b6b4ebca · outbound

This paper cites Proceedings of the 37th Annual Allerton Conference on Communication, Control, and Computing , pages=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Proceedings of the 37th Annual Allerton Conference on Communication, Control, and Computing , pages=

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T17:26:11.892259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54d82c16-9aa3-41b1-ab42-830c78f7a030 · outbound

This paper cites an unresolved cited work.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:26:12.466716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation acfe559d-d714-4ffa-ba50-b1f8b2d2e4af · outbound

This paper cites International Conference on Learning Representations , year=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? International Conference on Learning Representations , year=

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-10T17:26:11.902242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbfd33d4-60bf-425f-9738-8f7d67c90582 · outbound

This paper cites , journal=.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? , journal=

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:26:12.444222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 360ab4e9-2cef-4673-9bf0-e96ca78c0626 · outbound

This paper cites Text-Only Training for Image Captioning using Noise-Injected.

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding? Text-Only Training for Image Captioning using Noise-Injected

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:26:12.430218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers