Paper Citation Record · LEDGER

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks

As of 21 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.03627.

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

pith.paper-citation-record.v1
2506.03627 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:03:12.199715Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

verified exact0
verified fuzzy19
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 83156f2d-e4b6-4f77-8270-570859ddaa04 · outbound

This paper cites Language mod- els are few-shot learners,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Language mod- els are few-shot learners,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.609185Z

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 a6accd47-22dc-4072-8155-1758c416beab · outbound

This paper cites Palm: Scal- ing language modeling with pathways,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Palm: Scal- ing language modeling with pathways,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.601226Z

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 307d87de-fd82-4f44-aedf-047f2d9bedfb · outbound

This paper cites A survey on large language models for recommendation,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks A survey on large language models for recommendation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.593163Z

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 83572346-9b97-454e-9a9a-08bfbe40f4c2 · outbound

This paper cites Netprompt: Neural network prompting enhances event extraction in large language models,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Netprompt: Neural network prompting enhances event extraction in large language models,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.585169Z

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 750a47de-959f-4677-815a-534e850af80b · outbound

This paper cites Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:12.125447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 983c4d97-cf65-4b76-bbe7-70373890d4f8 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Chain-of-thought prompting elicits reasoning in large language models,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.576950Z

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 9f4b79cc-e13c-4fca-86d3-545de7d3ff15 · outbound

This paper cites Large language models are human-level prompt engineers,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Large language models are human-level prompt engineers,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.568950Z

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 550a8a82-c788-488b-b82d-006b182408a6 · outbound

This paper cites Reasoning Robustness of LLMs to Adversarial Typographical Errors.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Reasoning Robustness of LLMs to Adversarial Typographical Errors

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:12.134359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20ea3c6d-f486-4fa1-a6b1-d8d183f748bc · outbound

This paper cites An LLM can Fool Itself: A Prompt-Based Adversarial Attack.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks An LLM can Fool Itself: A Prompt-Based Adversarial Attack

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:12.137203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18148edc-4e91-4ac8-a2e6-08d7e57272d8 · outbound

This paper cites Promptrobust: Towards evaluating the robustness of large language models on adversarial prompts,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Promptrobust: Towards evaluating the robustness of large language models on adversarial prompts,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.561175Z

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 5b6dbd65-e13d-4d0c-a4ce-6b7cc45e1a00 · outbound

This paper cites Measure and improve robustness in NLP models: A survey,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Measure and improve robustness in NLP models: A survey,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.553159Z

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 9f8e9d78-203f-4f2b-bef4-f14474d2ddb0 · outbound

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

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:12.144778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15a8c7b8-1bc8-4541-b313-40cb341be1d2 · outbound

This paper cites Prompt learning for few-shot question answering via self-context data augmentation,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Prompt learning for few-shot question answering via self-context data augmentation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.545243Z

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 1c47f092-f6ee-4259-ba80-947d9fd055f5 · outbound

This paper cites Large lan- guage models are zero-shot reasoners,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Large lan- guage models are zero-shot reasoners,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:12.150201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4728ccd4-7a31-41cf-9f9f-7b38f28a8de1 · outbound

This paper cites Large language models as optimizers,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Large language models as optimizers,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.533492Z

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 d831d7b8-4a43-40c1-a6ef-121e920bef1a · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Training Verifiers to Solve Math Word Problems

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:12.154599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f915cb4-1eed-42ba-a950-0f34c9f72ec6 · outbound

This paper cites A Survey on In-context Learning.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks A Survey on In-context Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:12.157526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b36439a-39f9-4f6f-b532-09de9bc54e86 · outbound

This paper cites Adversarial attacks and defenses in machine learning-empowered communication systems and networks: A contemporary survey,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Adversarial attacks and defenses in machine learning-empowered communication systems and networks: A contemporary survey,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.525466Z

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 2f29dd02-10b9-4123-b3e1-e958477fcc96 · outbound

This paper cites Program induction by rationale generation: Learning to solve and explain algebraic word problems,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Program induction by rationale generation: Learning to solve and explain algebraic word problems,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.517249Z

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 afb56c2d-3096-4ab0-807d-a8e742f15a69 · outbound

This paper cites Parsing algebraic word problems into equations,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Parsing algebraic word problems into equations,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.507616Z

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 bdb4f8f3-6016-40fa-b9d9-257549d9958d · outbound

This paper cites Are NLP models really able to solve simple math word problems?,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Are NLP models really able to solve simple math word problems?,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.499112Z

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 11107f31-72a6-4a8c-a6dd-a015938ab8f1 · outbound

This paper cites Solving general arithmetic word problems,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Solving general arithmetic word problems,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.489861Z

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 2f672979-0e3f-44b0-a86f-105c5aa31732 · outbound

This paper cites Learning to solve arithmetic word problems with verb categorization,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Learning to solve arithmetic word problems with verb categorization,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.477599Z

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 de4a306e-65d1-4874-8926-9d73cce336b9 · outbound

This paper cites GPT-4o System Card.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks GPT-4o System Card

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:12.175549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bc96f30-9252-406f-99de-4d55edb2a4dc · outbound

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

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Training language models to follow instructions with human feedback,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:12.178427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9caba623-246e-443d-8aa4-6a61711a7094 · outbound

This paper cites OpenAI o1 System Card.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks OpenAI o1 System Card

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:12.181183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42e98503-fd3e-4237-a180-b18dfb4b1a9d · outbound

This paper cites Openai o3 and o4-mini system card,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Openai o3 and o4-mini system card,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:12.183905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 469e2940-05f4-48f0-bc45-6d8794a81321 · outbound

This paper cites Promptagent: Strategic planning with language models enables expert-level prompt optimization,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Promptagent: Strategic planning with language models enables expert-level prompt optimization,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.460533Z

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 5b6662ec-59ff-40ca-bd14-983913805e19 · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:12.189700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b6396ea-85e4-4023-ae28-89a02a09b163 · outbound

This paper cites CommonsenseQA: A question answering challenge targeting commonsense knowledge,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks CommonsenseQA: A question answering challenge targeting commonsense knowledge,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.350500Z

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 3c7906cb-991a-4f78-a122-b4fcc5ceca94 · outbound

This paper cites Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies,.

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:03:12.284759Z

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.

Pith citing papers

No inbound Pith citation observations are available.