Paper Citation Record · LEDGER

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study

As of 16 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2506.19773.

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

pith.paper-citation-record.v1
2506.19773 v2

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:28:47.163457Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T14:54:36.915852Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T14:58:32.445072Z

Reference resolution

48 of 48 outbound references displayed

verified exact0
verified fuzzy38
unresolved10
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f0a72b94-4b39-413b-b777-26712d6e6ec7 · outbound

This paper cites Knowledge Graphs.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Knowledge Graphs

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:50.008243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5ba564b2-5f82-4331-b257-34463ec54a84 · outbound

This paper cites A Comprehensive Survey on Automatic Knowl- edge Graph Construction.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study A Comprehensive Survey on Automatic Knowl- edge Graph Construction

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.916897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b4a5dfea-94ab-4375-846d-ea6e57c5a058 · outbound

This paper cites A survey of current approaches for mapping of relational databases to rdf.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study A survey of current approaches for mapping of relational databases to rdf

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.898744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 703bcec7-793d-4943-b542-59e579a67809 · outbound

This paper cites Rml: A generic language for integrated rdf mappings of heterogeneous data.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Rml: A generic language for integrated rdf mappings of heterogeneous data

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.848140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 651c343f-d514-4bf3-903b-3d321fba0094 · outbound

This paper cites Wikidata: A Free Collaborative Knowledge Base.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Wikidata: A Free Collaborative Knowledge Base

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.702762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 763a7e60-6664-4c69-9ee6-f85dd976d13a · outbound

This paper cites Research trends for the interplay between large language models and knowledge graphs.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Research trends for the interplay between large language models and knowledge graphs

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.684400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 20652f16-0471-4f94-b55f-93a3a77e397c · outbound

This paper cites Rodriguez-Méndez.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Rodriguez-Méndez

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.665673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 61afc7e1-d684-4fc6-b110-4ee82acc2c2b · outbound

This paper cites Language models as knowledge bases? In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, pages 2463–2473, Hong Kong, China, November 2019.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Language models as knowledge bases? In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, pages 2463–2473, Hong Kong, China, November 2019

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.581119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5ff98b62-597c-4004-87db-34109c57d3b2 · outbound

This paper cites Knowledge graph generation from text.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Knowledge graph generation from text

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.522105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 33601d89-a33f-4fdc-93c2-68c590dae921 · outbound

This paper cites Faisal Mahbub Chowdhury, Nandana Mihindukulasooriya, Owen Cornec, and Al- fio Massimiliano Gliozzo.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Faisal Mahbub Chowdhury, Nandana Mihindukulasooriya, Owen Cornec, and Al- fio Massimiliano Gliozzo

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.502870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3b5345e1-bd3e-40e9-98de-390ce9a1e960 · outbound

This paper cites Ex- ploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Ex- ploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.359124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3ebc1190-3e86-461a-b265-679ff87749f5 · outbound

This paper cites Re2G: Retrieve, rerank, generate.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Re2G: Retrieve, rerank, generate

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.309372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f78e3ea8-9626-4ee8-8ef5-6ade9a52cc80 · outbound

This paper cites From Local to Global: A Graph RAG Approach to Query-Focused Summarization.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T18:28:46.575685Z

Source-reported events for the cited work

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Observation bc4909fd-d5d6-4b0e-afb6-6c13eb2fcfb2 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Retrieval-augmented generation for knowledge-intensive nlp tasks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.257912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dc60351d-dae1-44ee-8873-abd2d778d960 · outbound

This paper cites Learning to summarize with human feedback.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Learning to summarize with human feedback

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T18:28:46.586205Z

Source-reported events for the cited work

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Observation 51bb161e-2a37-4c14-8bba-eeba038b867e · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T18:28:46.591890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 137f918e-af94-4d34-96a0-10433f23a515 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.153899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cc373454-49b7-4a84-8263-d4e7def9cc4d · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.134132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bdbb5b3d-c8d9-45e6-9640-67bebb633f30 · outbound

This paper cites PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.091390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f6937367-cbf1-43ed-99e1-7b0fcf0ae5b0 · outbound

This paper cites Ask me anything: A simple strategy for prompting language models.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Ask me anything: A simple strategy for prompting language models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.029368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation baeb5413-6737-425e-9f33-9b268590aaf2 · outbound

This paper cites Beyond english: The impact of prompt translation strategies across languages and tasks in multilingual llms.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Beyond english: The impact of prompt translation strategies across languages and tasks in multilingual llms

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:49.009066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fdce67d8-c35e-4b92-b991-4b0d1a9d6140 · outbound

This paper cites A systematic survey of automatic prompt optimization tech- niques.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study A systematic survey of automatic prompt optimization tech- niques

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T18:28:46.792663Z

Source-reported events for the cited work

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Observation 5ad7fafa-c6c0-430d-a583-42906290b944 · outbound

This paper cites A Survey of Automatic Prompt Engineering: An Optimization Perspective.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study A Survey of Automatic Prompt Engineering: An Optimization Perspective

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T18:28:46.798143Z

Source-reported events for the cited work

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Observation 4257377e-661d-42b7-8aef-ee8753152530 · outbound

This paper cites Open information extraction from the web.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Open information extraction from the web

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:48.879135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8ec90f16-0a07-4e02-a222-8ec62fcdd75a · outbound

This paper cites Knowledge Graph Embedding with Hierar- chical Relation Structure.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Knowledge Graph Embedding with Hierar- chical Relation Structure

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:48.805184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 453ec30e-e74d-4311-b9e2-47c85e8f0da8 · outbound

This paper cites Open knowl- edge graphs canonicalization using variational autoencoders.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Open knowl- edge graphs canonicalization using variational autoencoders

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:48.709918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2a3b55f0-42d2-4c67-82de-39dc0964f55b · outbound

This paper cites Generative relation linking for question answering over knowledge bases.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Generative relation linking for question answering over knowledge bases

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:48.692973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7f3edb0c-dae5-4b12-945d-4bc7172e423f · outbound

This paper cites Training language models to follow instructions with human feedback.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Training language models to follow instructions with human feedback

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T18:28:46.823410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33f60090-a3e5-4e78-874d-8556caf39dea · outbound

This paper cites Deep reinforcement learning from human preferences.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Deep reinforcement learning from human preferences

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T18:28:46.829420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04ddcbbc-a5cc-413c-98e3-96b78a9ca008 · outbound

This paper cites Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? EMNLP 2022, 2022.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? EMNLP 2022, 2022

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:48.543335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 87a2f38d-fed5-473f-b13f-f34122698353 · outbound

This paper cites An Explanation of In-context Learning as Implicit Bayesian Inference.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:48.521941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 09f6862b-d3c0-4521-ac9d-377401e33f24 · outbound

This paper cites Enguix, and Kusum Lata.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Enguix, and Kusum Lata

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:48.388589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 634b5b59-34a3-4d73-8329-5a9a943dcb28 · outbound

This paper cites A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm

Reference 33

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Source-reported events for the cited work

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Observation bfd8b7f1-fac9-4f46-b880-9131a1dbe7c0 · outbound

This paper cites Fairness-guided few-shot prompting for large language models.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Fairness-guided few-shot prompting for large language models

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:48.370393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 90fc8f9a-073c-4dc5-ad0c-2bcba7d403a9 · outbound

This paper cites Gradient Descent.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Gradient Descent

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:48.203420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fdb07435-3823-48c3-980c-3a2462448755 · outbound

This paper cites Introducing MAPO: Momentum-Aided Gradient Descent Prompt Optimization.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Introducing MAPO: Momentum-Aided Gradient Descent Prompt Optimization

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:48.156519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fa1119d0-bec4-4474-af0a-acf0ca6ce09c · outbound

This paper cites Optimizing generative ai by backpropagating language model feedback.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Optimizing generative ai by backpropagating language model feedback

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:48.110894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 14b3d8e4-7971-4d23-8dff-0d4dcb0df446 · outbound

This paper cites Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:47.948873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 72fb413d-5e5f-40b2-b968-0d5b3ce42656 · outbound

This paper cites Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, and Christopher Ré.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, and Christopher Ré

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:47.930984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fcb44203-f4b6-4abb-9511-32d4fc112be9 · outbound

This paper cites Evoprompt: Connecting llms with evolutionary algorithms yields powerful prompt optimizers.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Evoprompt: Connecting llms with evolutionary algorithms yields powerful prompt optimizers

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:47.796698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cfc9da92-bfc7-4b40-a997-20af7969c906 · outbound

This paper cites Large Language Models are Human-Level Prompt Engineers.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Large Language Models are Human-Level Prompt Engineers

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:47.692896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 13cd64fa-9ed2-4bba-ac26-034ec80e4f2b · outbound

This paper cites Query-dependent prompt evaluation and optimization with offline inverse rl.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Query-dependent prompt evaluation and optimization with offline inverse rl

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:47.671383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 00b88803-2c41-4637-8c93-76fe269451fe · outbound

This paper cites Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T18:28:47.047786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96fdc1ec-dbf6-45bd-968b-f4ce74875d9c · outbound

This paper cites DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T18:28:47.093156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92f98df9-360b-4f76-ad9d-57b8d5215fb9 · outbound

This paper cites Exploiting asymmetry for synthetic training data generation: SynthIE and the case of information extraction.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Exploiting asymmetry for synthetic training data generation: SynthIE and the case of information extraction

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:47.579552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 68b3a8a2-1980-4eae-a434-96ae30b2adf0 · outbound

This paper cites REBEL: Relation extraction by end-to-end language generation.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study REBEL: Relation extraction by end-to-end language generation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:47.484739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9794cf37-0077-4a7f-a50d-4e8e24e9974d · outbound

This paper cites Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:47.463932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 90d86f3b-5b89-4e66-9821-55f9795c9521 · outbound

This paper cites Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages.

Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:47.443121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation c511d676-7c56-4c8f-93bc-2b64950ebc1d · inbound

Object Aligner: A Configurable JSON Schema Similarity Score for Graphs, Applied to LLM Prompt Optimization cites this paper.

Object Aligner: A Configurable JSON Schema Similarity Score for Graphs, Applied to LLM Prompt Optimization Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:58:32.446550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.