An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts | Paper Citation Record

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This paper cites Available: https://figshare.com/s/930b08c981b41f28470c.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Available: https://figshare.com/s/930b08c981b41f28470c

Reference 1

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This paper cites Large Language Models as Software Components: A Taxonomy for LLM-Integrated Applications.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Large Language Models as Software Components: A Taxonomy for LLM-Integrated Applications

Reference 2

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This paper cites Large language models: A comprehensive survey of its applications, challenges, limitations, and future prospects.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Large language models: A comprehensive survey of its applications, challenges, limitations, and future prospects

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Technique improves the reasoning capabilities of large language models,

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This paper cites PromptSet: A Programmer's Prompting Dataset.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts PromptSet: A Programmer's Prompting Dataset

Reference 5

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This paper cites Auto-debias: Debiasing masked language models with automated biased prompts,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Auto-debias: Debiasing masked language models with automated biased prompts,

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This paper cites Precisedebias: An automatic prompt engineering approach for generative ai to mitigate image demographic biases,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Precisedebias: An automatic prompt engineering approach for generative ai to mitigate image demographic biases,

Reference 7

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This paper cites Automatic Prompt Optimization with "Gradient Descent" and Beam Search.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 8

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This paper cites Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

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This paper cites Language models get a gender makeover: Mitigating gender bias with few-shot data interventions,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Language models get a gender makeover: Mitigating gender bias with few-shot data interventions,

Reference 10

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This paper cites Formalizing and benchmarking prompt injection attacks and defenses,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Formalizing and benchmarking prompt injection attacks and defenses,

Reference 11

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This paper cites Promptcare: Prompt copyright protection by watermark injection and verification,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Promptcare: Prompt copyright protection by watermark injection and verification,

Reference 12

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This paper cites Don’t stop pretraining? make prompt-based fine-tuning powerful learner,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Don’t stop pretraining? make prompt-based fine-tuning powerful learner,

Reference 13

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This paper cites An analysis of large language models: their impact and potential applications,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts An analysis of large language models: their impact and potential applications,

Reference 14

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correction dated 2024-07-16. Source: crossref record 10.1007/s10115-024-02157-9->10.1007/s10115-024-02120-8:correction, observed 2026-07-11T03:08:43.809337+00:00. This notice travels one citation hop only.

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This paper cites Large language models: Their success and impact,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Large language models: Their success and impact,

Reference 15

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This paper cites GPT-4 Technical Report.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts GPT-4 Technical Report

Reference 16

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This paper cites The Llama 3 Herd of Models.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts The Llama 3 Herd of Models

Reference 17

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This paper cites True Few-Shot Learning with Prompts -- A Real-World Perspective.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts True Few-Shot Learning with Prompts -- A Real-World Perspective

Reference 18

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This paper cites How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Fair Models in Credit: Intersectional Discrimination and the Amplification of Inequity

Reference 20

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This paper cites Evaluating racial bias in large language models: The necessity for “smoky.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Evaluating racial bias in large language models: The necessity for “smoky

Reference 21

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Bias and Fairness in Large Language Models: A Survey

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This paper cites Marked personas: Using natural language prompts to measure stereotypes in language models,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Marked personas: Using natural language prompts to measure stereotypes in language models,

Reference 23

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Large language models propagate race-based medicine,

Reference 24

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Dialect prejudice predicts AI decisions about people's character, employability, and criminality

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts An Early Categorization of Prompt Injection Attacks on Large Language Models

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts How strangers got my email address from chatgpt’s model,

Reference 27

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts PLeak: Prompt Leaking Attacks against Large Language Model Applications

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Prompt injection attacks and defenses in llm-integrated applications,

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Prompt Injection attack against LLM-integrated Applications

Reference 30

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Assessing Prompt Injection Risks in 200+ Custom GPTs

Reference 31

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts ARB: Advanced Reasoning Benchmark for Large Language Models

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Benchmarking Large Language Models for Math Reasoning Tasks

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Prompt Design and Engineering: Introduction and Advanced Methods

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Synthetic data (almost) from scratch: Generalized instruction tuning for language models,

Reference 38

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts spaCy: Industrial-strength Natural Language Processing in Python,

Reference 39

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Gender bias in big data analysis,

Reference 40

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An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Available: https://ssir.org/articles/entry/when_good_algorithms_go_sexist_why_and_how_to_advance_ai_ gender_equity

Reference 41

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This paper cites From gender biases to gender-inclusive design: An empirical investigation,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts From gender biases to gender-inclusive design: An empirical investigation,

Reference 42

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This paper cites Mind the gap: gender, micro-inequities and barriers in software development,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Mind the gap: gender, micro-inequities and barriers in software development,

Reference 43

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This paper cites Gender differences and bias in open source: pull request acceptance of women versus men,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Gender differences and bias in open source: pull request acceptance of women versus men,

Reference 44

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This paper cites All the ways hiring algorithms can introduce bias,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts All the ways hiring algorithms can introduce bias,

Reference 45

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Observation a671278d-1b64-4f0d-940f-42e7b227ed7e · outbound

This paper cites The risk of racial bias in hate speech detection,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts The risk of racial bias in hate speech detection,

Reference 46

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This paper cites Dissecting racial bias in an algorithm used to manage the health of populations,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Dissecting racial bias in an algorithm used to manage the health of populations,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.854662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fddea232-cff2-490e-9b71-b95df0d4f634 · outbound

This paper cites Lgbtq+ in workplace: a systematic review and reconsideration,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Lgbtq+ in workplace: a systematic review and reconsideration,

Reference 48

Resolution: verified exact; doi, observed 2026-08-10T17:14:06.794361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 03b0ff6e-fb6f-43be-b424-e8d3e1e64739 · outbound

This paper cites Identifying the Prevalence of Gender Biases among the Computing Organizations.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Identifying the Prevalence of Gender Biases among the Computing Organizations

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:14:07.386492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d215c163-92b5-46c8-81fe-8f651d3dab26 · outbound

This paper cites WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Models.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.645368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28c5ec2b-6982-4c84-8cd0-2c607a301492 · outbound

This paper cites Language models are unsupervised multitask learners.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Language models are unsupervised multitask learners

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.845355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ae014fbd-db33-41ea-b0a9-ad98ae873e47 · outbound

This paper cites Language models are few-shot learners,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Language models are few-shot learners,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.835851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fb3e956e-d655-48f6-991d-6352a80d7461 · outbound

This paper cites Large Language Models as Optimizers.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Large Language Models as Optimizers

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.655877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bd00904-5d0d-4675-962b-b513b7d6a7b8 · outbound

This paper cites Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.659718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1da25d1d-2e0b-46ac-8e7d-96318d2692ef · outbound

This paper cites PromptWizard: Task-Aware Prompt Optimization Framework.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts PromptWizard: Task-Aware Prompt Optimization Framework

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.663336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 416e0372-09fc-4503-adc4-6dbb43b327de · outbound

This paper cites Choice over control: How users write with large language models using diegetic and non-diegetic prompting,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Choice over control: How users write with large language models using diegetic and non-diegetic prompting,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.667128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 761f2992-b105-444a-8adc-0b4ab0a0ea8e · outbound

This paper cites Signed-prompt: A new approach to prevent prompt injection attacks against llm-integrated applications,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Signed-prompt: A new approach to prevent prompt injection attacks against llm-integrated applications,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.826256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6f00fc47-e5b9-4b82-9945-2331dffb882b · outbound

This paper cites Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.673638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3903aa3-4306-48e8-be3f-18e4b1f19d92 · outbound

This paper cites Prompt shields in azure ai content safety - azure ai services,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Prompt shields in azure ai content safety - azure ai services,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.816369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6a6a38fb-558e-4474-babe-1dd8c56435ca · outbound

This paper cites Why johnny can’t prompt: How non-ai experts try (and fail) to design llm prompts,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Why johnny can’t prompt: How non-ai experts try (and fail) to design llm prompts,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.680236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc628f94-bb25-4a93-a901-a9155d704709 · outbound

This paper cites Prompt engineering,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Prompt engineering,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.806755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 11d239eb-9ba5-4c33-98f8-b2ae485c9616 · outbound

This paper cites Prompt engineering overview,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Prompt engineering overview,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.796428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 661dd609-10db-445d-8996-2726a3a131eb · outbound

This paper cites Bleu: a method for automatic evaluation of machine translation,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Bleu: a method for automatic evaluation of machine translation,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.689002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13126b14-6006-49fb-ab24-fc41fef55698 · outbound

This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Sentence-bert: Sentence embeddings using siamese bert-networks,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.786794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5835b4df-3f25-47ca-9871-33e6e88e9004 · outbound

This paper cites GLEU: Automatic evaluation of sentence-level fluency,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts GLEU: Automatic evaluation of sentence-level fluency,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.776101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bbd28e9d-18e3-4aae-af33-89194dc51466 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Judging llm-as-a-judge with mt-bench and chatbot arena,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.766297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9518a025-e7bf-4a9a-a38a-87a988cf84da · outbound

This paper cites “call me sexist, but.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts “call me sexist, but

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.756086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 84bfe590-7e9d-474e-aefd-ddb5644650b0 · outbound

This paper cites Xhate-999: Analyzing and detecting abusive language across domains and languages,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Xhate-999: Analyzing and detecting abusive language across domains and languages,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.746421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3fefbbf6-6932-4672-88b2-63f5606d29c9 · outbound

This paper cites What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.713339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ed4887e-342d-4b00-b10e-c89521fa2976 · outbound

This paper cites Pubmedqa: A dataset for biomedical research question answering,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Pubmedqa: A dataset for biomedical research question answering,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.736630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 283701ba-0876-4cdb-944e-603dfa8a0359 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Training Verifiers to Solve Math Word Problems

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.720574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 215903d3-9cf2-43d8-9d55-428584cad178 · outbound

This paper cites Ethos: Rectifying language models in orthogonal parameter space,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Ethos: Rectifying language models in orthogonal parameter space,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.727964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d9fc60e2-ee5b-4cd9-9020-79a80cf88507 · outbound

This paper cites Get to the point: Summarization with pointer-generator networks,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Get to the point: Summarization with pointer-generator networks,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.719050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 175825d5-b92e-46d0-bec1-b5422b44f11b · outbound

This paper cites English-spanish translation dataset,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts English-spanish translation dataset,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.709051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 853bab0d-626e-40f5-92f8-71fcea4e2edf · outbound

This paper cites Mining & mastering the art of english corrections,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Mining & mastering the art of english corrections,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.699442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a23f7e5e-3319-43ba-9182-9f4a13968102 · outbound

This paper cites Decoding symbolism in language models,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Decoding symbolism in language models,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.689321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9b5c9314-d405-4702-b66d-1451814f0216 · outbound

This paper cites Universal and transferable adversarial attacks on aligned language models,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Universal and transferable adversarial attacks on aligned language models,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.679020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation af08d188-5d3d-4470-9023-86ad78d46054 · outbound

This paper cites Jailbreaking Black Box Large Language Models in Twenty Queries.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.744173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 773f2da0-0e8e-41ac-b507-e7050b94abe2 · outbound

This paper cites Ignore Previous Prompt: Attack Techniques For Language Models.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Ignore Previous Prompt: Attack Techniques For Language Models

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.747481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9800494-366f-4462-816c-f31594f2bb64 · outbound

This paper cites Copiloting the copilots: Fusing large language models with completion engines for automated program repair,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Copiloting the copilots: Fusing large language models with completion engines for automated program repair,

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.751070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 153c217e-d928-4ddd-b605-52aec7570809 · outbound

This paper cites Automatic semantic augmentation of language model prompts (for code summarization),.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Automatic semantic augmentation of language model prompts (for code summarization),

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.757291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77eee360-f28d-47e1-9c40-eadced687773 · outbound

This paper cites Prompting is all you need: Automated android bug replay with large language models,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Prompting is all you need: Automated android bug replay with large language models,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.760264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4bbedf5-479b-407b-bdb2-ee6aa1a9703b · outbound

This paper cites Gpt-4o system card,.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Gpt-4o system card,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:14:07.669100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bc6ba37f-0315-40fc-bccd-f2e2f6a9d6bd · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T17:14:06.695117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers