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Paper Citation Record · LEDGER

Promptagator: Few-shot Dense Retrieval From 8 Examples

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:2209.11755.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2209.11755 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:31:28.015019Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-01T10:45:42.845740Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 73419d61-d39f-44ce-bdef-54c08ae84ea1 · inbound

Text Embeddings by Weakly-Supervised Contrastive Pre-training cites this paper.

Text Embeddings by Weakly-Supervised Contrastive Pre-training Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T04:54:03.993032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-11T04:54:03.524365Z digest=sha256:e36acedadcbb50227ecfe7affcc84ee9238e8b3ef1045635f62a9dbe41ff4f8b

Observation 730da162-323a-4e4a-900d-76e7339fa3f4 · inbound

RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze! cites this paper.

RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze! Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T23:40:11.069245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-15T23:40:11.018808Z digest=sha256:14dc64ba15cc4a6f9cf7d33f47d3d561e065460f02cda1dd04840aa9d761a331

Observation 62537659-2ad4-4bef-aac3-d45214edc6ae · inbound

Retrieval-Augmented Generation for Large Language Models: A Survey cites this paper.

Retrieval-Augmented Generation for Large Language Models: A Survey Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-24T05:13:57.202696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-24T05:10:25.171044Z digest=sha256:5de7650499d8e2e60690ac98c59c6b71b1d76be59a4d316acb67a657bc22504d

Observation d6f630d4-a812-4c16-ba3c-eff4222693aa · inbound

Can Generative LLMs Create Query Variants for Test Collections? An Exploratory Study cites this paper.

Can Generative LLMs Create Query Variants for Test Collections? An Exploratory Study Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-10T04:31:28.015019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T04:31:28.015019Z digest=sha256:f64f9fa9e5550652b36240a8b4898eab0dc64851f2964f999f6cd5148837964e

Observation ff5b63e8-d00d-4dbe-be38-bf161f0824f3 · inbound

RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects cites this paper.

RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-09T23:48:53.889785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T23:48:53.889785Z digest=sha256:ff48d1a0a83ad52f44af7dc6af07cc3012245f8b9f51be491a90d3a91cea5ef9

Observation 9451f71d-2ca3-40ce-a76d-e5ca6b3e134e · inbound

RankLLM: A Python Package for Reranking with LLMs cites this paper.

RankLLM: A Python Package for Reranking with LLMs Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T14:20:38.717755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:20:38.717755Z digest=sha256:24eaeea0dd1ee58786494fa587c4d6c14de76138d07c6cf2c04a293451369791

Observation 4e076b39-c6d5-40b1-8743-b222f50a13ed · inbound

From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems cites this paper.

From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-19T11:52:16.359906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-19T11:49:36.574471Z digest=sha256:8d8316be111d4a78b6bbf5fee405425dd8144ed6dfedaa341de2cf6ef6c26a83

Observation 5a13a3a5-71f9-46f6-86bc-4acf18f5c5ed · inbound

Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis cites this paper.

Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-06T16:58:23.526840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:58:23.526840Z digest=sha256:4481fd7fe01d79d88991825d3fd9affd1e6612905bc3c8577c3b3398f12f509f

Observation a8fab6a7-e0a7-4cda-95f6-317433861119 · inbound

DR.EHR: Dense Retrieval for Electronic Health Record with Knowledge Injection and Synthetic Data cites this paper.

DR.EHR: Dense Retrieval for Electronic Health Record with Knowledge Injection and Synthetic Data Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T14:37:21.794884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:37:21.794884Z digest=sha256:4839699e5f92700f26857045390cfbb8ffd2aa638e57f68587270ac34bb9c630

Observation fd1eeb30-40f6-4175-ac13-7128e3dbbf9f · inbound

Negative Matters: Multi-Granularity Hard-Negative Synthesis and Anchor-Token-Aware Pooling for Enhanced Text Embeddings cites this paper.

Negative Matters: Multi-Granularity Hard-Negative Synthesis and Anchor-Token-Aware Pooling for Enhanced Text Embeddings Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T13:16:26.739409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:16:26.739409Z digest=sha256:a9e670d5d609fc9579c36712b98648fda2d23208f64db7d8b23c8c1a478d4871

Observation 14ac27db-74e9-43e8-9d4b-cedd897f43ae · inbound

More Than Efficiency: Embedding Compression Improves Domain Adaptation in Dense Retrieval cites this paper.

More Than Efficiency: Embedding Compression Improves Domain Adaptation in Dense Retrieval Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-03T09:35:14.601529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:35:14.601529Z digest=sha256:ac2980a99eb29c47d8873ecc4f9808f9859fc30733b9ea359c66db8e8a276287

Observation 7f6fafa5-1b21-4dbd-ac14-8a532bd45b2f · inbound

Are LLM-Based Retrievers Worth Their Cost? An Empirical Study of Efficiency, Robustness, and Reasoning Overhead cites this paper.

Are LLM-Based Retrievers Worth Their Cost? An Empirical Study of Efficiency, Robustness, and Reasoning Overhead Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-13T17:23:02.671176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-13T17:19:40.630283Z digest=sha256:0038650ec2dcf70e5c6c2e006c607729ed468ce0ff89481a324a3de7533e1905

Observation 8b37d678-c1cc-46b6-9875-8ce972c8f38a · inbound

Dynamic Ranked List Truncation for Reranking Pipelines via LLM-generated Reference-Documents cites this paper.

Dynamic Ranked List Truncation for Reranking Pipelines via LLM-generated Reference-Documents Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 5

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T08:50:59.394340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T16:27:04.127654Z digest=sha256:654aabbb7486b6dc50528212b2411f3ad40286e3bf57cbe7c38d88cd80780d44

Observation cf95690a-3dde-49c2-9c12-737b33a5ce6f · inbound

ARHN: Answer-Centric Relabeling of Hard Negatives with Open-Source LLMs for Dense Retrieval cites this paper.

ARHN: Answer-Centric Relabeling of Hard Negatives with Open-Source LLMs for Dense Retrieval Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:11:12.421358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T16:10:43.392191Z digest=sha256:2833595ab4632220c3f7828730192db2bb47dbe103885a8efa9305734ff3286e

Observation 91513a82-c4f4-45e4-93b9-3e32963ab8a8 · inbound

UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval cites this paper.

UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-12T00:06:16.064823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-07T15:51:41.243646Z digest=sha256:41e03a7a22588598ad514ef8112f42f391f7cbbcdffd64f5f5fe1244d18fac8f

Observation 0ebcb719-ffd6-44cd-a084-d91ce7b66f9c · inbound

Localization Boosting for Growth Markets: Mitigating Cross-Locale Behavioral Bias in Learning-to-Rank cites this paper.

Localization Boosting for Growth Markets: Mitigating Cross-Locale Behavioral Bias in Learning-to-Rank Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 10

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metadata mismatch
arxiv_id, observed 2026-05-13T02:57:09.167473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-13T02:55:40.840979Z digest=sha256:125782f98edf9c3e9e5a8d17029c1fe34188762788b72a4295b10cc4f2dbf153

Observation 972bfdec-47b2-4f63-8617-93b485f88710 · inbound

SPECTRA: Synthetic IR Test Collections with Relevance Oracles and Controlled Distractor Diagnostics cites this paper.

SPECTRA: Synthetic IR Test Collections with Relevance Oracles and Controlled Distractor Diagnostics Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-06-28T20:42:37.232638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-28T20:37:00.077796Z digest=sha256:ce5390cacac873a2f9cf9e073346ec8625d122fda27bc95a4546655b3bd3492a

Observation c66d7f43-d1c1-49ad-81a4-313c3e4ff0f0 · inbound

Self-Study Reconsidered: The Hidden Fragility of Learning from Self-Generated QA cites this paper.

Self-Study Reconsidered: The Hidden Fragility of Learning from Self-Generated QA Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T10:45:42.847421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-07-01T05:07:58.441326Z digest=sha256:561ee06a02ac81c28dcd380a769615211b25979cd8a410942c9323f2cba41be5

Observation 20443763-e8d5-4780-9904-8976e1d8cff8 · inbound

Finding the Right Tables and Columns: A Benchmark and Corpus-Adaptive Embeddings for SQL Schema Retrieval cites this paper.

Finding the Right Tables and Columns: A Benchmark and Corpus-Adaptive Embeddings for SQL Schema Retrieval Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-02T05:38:04.339592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T05:38:04.339592Z digest=sha256:f2606358369f27ad058f5d0880e37a3406e21a7a11a8cf5d11b6d0f51512745e

Observation 19b23f6b-4fc3-4620-a286-fca689bffe96 · inbound

Improving Rare Medication Recommendation with Counterfactual Data Augmentation and Large Language Models cites this paper.

Improving Rare Medication Recommendation with Counterfactual Data Augmentation and Large Language Models Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-01T09:10:46.133985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T09:10:46.133985Z digest=sha256:1963a0aaec1a3a3f322ab4901a370da7043315ca969580de58daf37be255a7dc

Observation d540b2d2-1aa6-4a3e-916b-f7e5bf724dfb · inbound

Bekko Embedding: Parameter-Efficient Multilingual Retrieval with Ultra-Compact Encoders cites this paper.

Bekko Embedding: Parameter-Efficient Multilingual Retrieval with Ultra-Compact Encoders Promptagator: Few-shot Dense Retrieval From 8 Examples

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-01T03:15:58.337899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T03:15:58.337899Z digest=sha256:935b1419c653fadcf773710e8574480fdda8aa5e69cec87d51ecb2408d8b28a8