Paper Citation Record · LEDGER

Promptagator: Few-shot Dense Retrieval From 8 Examples

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 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 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:20:38.717755Z

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 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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.

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 2026-08-09T06:31:02.800959+00:00.

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.

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.

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.

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.

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 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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

Resolution: 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 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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.

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.

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.