Paper Citation Record · LEDGER

A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2407.12994.

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

pith.paper-citation-record.v1
2407.12994 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T21:39:46.034800Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:27:21.998620Z

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 7d86f605-d7fc-4a28-8f6b-1a3d0650cbd3 · inbound

Concept Navigation and Classification via Open-Source Large Language Model Processing cites this paper.

Concept Navigation and Classification via Open-Source Large Language Model Processing A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-08T21:39:46.034800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54e27e64-ef25-47d3-b589-6f7491aa67e8 · inbound

Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference cites this paper.

Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-23T00:07:17.316661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8678f145-7c79-419d-bf0e-4a486545b617 · inbound

Computational Experiments in Number Theory cites this paper.

Computational Experiments in Number Theory A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-22T19:32:00.923929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 805d83e0-d2c2-4f36-a46e-da782bc4e606 · inbound

Incorporating Token Usage into Prompting Strategy Evaluation cites this paper.

Incorporating Token Usage into Prompting Strategy Evaluation A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T15:34:00.164806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83d0ca35-2fce-4ada-9a34-7bd695402526 · inbound

Large Language Models in the Task of Automatic Validation of Text Classifier Predictions cites this paper.

Large Language Models in the Task of Automatic Validation of Text Classifier Predictions A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:30:59.209312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 315393e8-71c1-4584-9392-f9b52a07cb8d · inbound

Extracting Research Instruments from Educational Literature Using LLMs cites this paper.

Extracting Research Instruments from Educational Literature Using LLMs A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:55.770476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d456d0a4-8489-41ac-9362-e85478deea84 · inbound

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges cites this paper.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:25.036937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f57cdd04-7be8-4337-a85e-b4850550b781 · inbound

Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation cites this paper.

Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T16:27:23.182275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2dde3a87-f714-4739-8d98-b3a57938d4c0 · inbound

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software cites this paper.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T16:26:35.052035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a27d1e6c-7ec9-4b0a-8f00-1a0752cf9c86 · inbound

PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses cites this paper.

PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:00:02.957372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 29debcdb-eb0d-4b42-8c77-c7203e8be6c8 · inbound

MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval cites this paper.

MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:30:48.221345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 371b3f4a-c21a-43b9-b6b4-faeff5350289 · inbound

MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval cites this paper.

MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T06:07:40.107621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac0371a6-e361-4a6e-aed6-01dcf103de7d · inbound

Prompt-Driven Code Summarization: A Systematic Literature Review cites this paper.

Prompt-Driven Code Summarization: A Systematic Literature Review A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:35:18.579889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3c8e4f63-fedf-4a55-8527-0aa8e4a1169b · inbound

Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs cites this paper.

Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 91

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:51:04.732401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 50d2cb7b-7708-4f05-9e5d-bf11da5a7375 · inbound

VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation cites this paper.

VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 225

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T17:06:04.110646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a4750a5a-6b29-42be-abc2-b24eb0d1e65a · inbound

A Taxonomy of Single-Turn Textual Prompt Patterns cites this paper.

A Taxonomy of Single-Turn Textual Prompt Patterns A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:27:22.000353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b5baf02a-2e84-47a7-bd02-b0307352f9ac · inbound

Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution cites this paper.

Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T00:20:37.229508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.