Paper Citation Record · LEDGER

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines

As of 23 August 2026, this Paper Citation Record lists 82 of 82 outbound references and 2 inbound Pith citation observations for arXiv:2504.14738.

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

pith.paper-citation-record.v1
2504.14738 v1

Coverage vector

measured 82 of 82 reference resolution

Typed states for the displayed outbound observations.

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measured 84 of 84 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T00:56:24.412552Z

Reference resolution

82 of 82 outbound references displayed

verified exact0
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External citation measurements

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Outbound references

Observation b901b01a-4ed9-4edf-8167-b58710e01634 · outbound

This paper cites an unresolved cited work.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Unresolved cited work

Reference 1

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Observation f5ff3c12-5408-45fb-a58d-8e2c86064f5f · outbound

This paper cites A survey on evaluation of large language models.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines A survey on evaluation of large language models

Reference 2

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Observation 488a3918-316b-4607-8253-81497ef95174 · outbound

This paper cites Benchmarking large language models in retrieval- augmented generation.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Benchmarking large language models in retrieval- augmented generation

Reference 3

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Observation ae44640f-54c0-42ad-8b8d-c6c75d58a771 · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Scaling Instruction-Finetuned Language Models

Reference 4

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Observation 3e65baf5-0e50-4cb5-8077-dfbe9a491c7b · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Training Verifiers to Solve Math Word Problems

Reference 5

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Reference 6

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Source-reported events for the cited work

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Observation b7c47f3c-b09d-4a1a-8492-6ef813bf6831 · outbound

This paper cites Building guardrails for large language models, 2024.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Building guardrails for large language models, 2024

Reference 7

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Observation 79d0aa8e-347a-4190-9b11-0622c6381185 · outbound

This paper cites Position: Building guardrails for large lan- guage models requires systematic design.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Position: Building guardrails for large lan- guage models requires systematic design

Reference 8

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Observation 06ce5c9a-9035-4956-b1e2-870a58381542 · outbound

This paper cites The Need for Guardrails with Large Language Models in Medical Safety-Critical Settings: An Artificial Intelligence Application in the Pharmacovigilance Ecosystem.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines The Need for Guardrails with Large Language Models in Medical Safety-Critical Settings: An Artificial Intelligence Application in the Pharmacovigilance Ecosystem

Reference 9

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Observation f5278d8e-716f-458d-a024-8f3a3e7798bc · outbound

This paper cites Measuring Massive Multitask Language Understanding.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Measuring Massive Multitask Language Understanding

Reference 10

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Observation ad2f9335-2a42-4c3b-97cd-50c8ca27ec14 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 11

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Observation a5d4ff67-f162-466b-bc8f-2028ad529c15 · outbound

This paper cites A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Reference 12

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions, 2023

Reference 13

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Observation 36e3c33a-0e1e-4dae-b339-85e612accfd8 · outbound

This paper cites Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Reference 14

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This paper cites Beavertails: To- wards improved safety alignment of llm via a human- preference dataset.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Beavertails: To- wards improved safety alignment of llm via a human- preference dataset

Reference 15

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Unresolved cited work

Reference 16

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Observation 3720bc09-8959-4fd2-9358-e9048911c4a6 · outbound

This paper cites Calibrated Language Models Must Hallucinate.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Calibrated Language Models Must Hallucinate

Reference 17

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Unresolved cited work

Reference 18

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Observation e0c8dc2e-15ba-4271-ba5f-716384b8a77c · outbound

This paper cites Prometheus: Inducing fine-grained evaluation capa- bility in language models.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Prometheus: Inducing fine-grained evaluation capa- bility in language models

Reference 19

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This paper cites Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models

Reference 20

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This paper cites Evallm: Interactive evaluation of large language model prompts on user-defined cri- teria.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Evallm: Interactive evaluation of large language model prompts on user-defined cri- teria

Reference 21

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Observation 274f6e81-4c26-4c67-b168-473a6b47cc06 · outbound

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

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Reference 22

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Observation 0e832080-5ac4-4fd8-8160-81f268867e7f · outbound

This paper cites Openassistant conversations- democratizing large language model alignment.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Openassistant conversations- democratizing large language model alignment

Reference 23

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines The power of scale for parameter-efficient prompt tuning, 2021

Reference 24

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Generative Judge for Evaluating Alignment

Reference 25

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This paper cites Large Language Models are Zero-Shot Reasoners.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Large Language Models are Zero-Shot Reasoners

Reference 26

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines we need structured output

Reference 27

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This paper cites AgentBench: Evaluating LLMs as Agents.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines AgentBench: Evaluating LLMs as Agents

Reference 28

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Decoupled weight decay regularization, 2019

Reference 29

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Observation 210b601d-7eac-4dc7-b26d-3a8e452fc914 · outbound

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 31

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Training language models to follow instructions with human feedback

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies

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This paper cites Nemo guardrails: A toolkit for controllable and safe llm applications with programmable rails, 2023.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Nemo guardrails: A toolkit for controllable and safe llm applications with programmable rails, 2023

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Building a Domain-specific Guardrail Model in Production

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines GPT-4 Technical Report

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This paper cites Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences

Reference 38

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

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

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This paper cites InFoBench: Evaluating Instruction Following Ability in Large Language Models.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines InFoBench: Evaluating Instruction Following Ability in Large Language Models

Reference 42

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization

Reference 43

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 44

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Chain-of-thought prompting elicits rea- soning in large language models

Reference 46

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Towards better evaluation of instruction-following: A case-study in summariza- tion

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Weinberger, and Yoav Artzi

Reference 48

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Reference 49

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Observation 135fee25-b8ff-4b08-8f6c-1c2e3bb68252 · outbound

This paper cites Llama: Open and efficient foundation language mod- els, 2023.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Llama: Open and efficient foundation language mod- els, 2023

Reference 50

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Observation 6fac03f7-f32e-43d3-9057-4cc1acc73170 · outbound

This paper cites Replacing judges with juries: Evaluating llm genera- tions with a panel of diverse models, 2024.

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Replacing judges with juries: Evaluating llm genera- tions with a panel of diverse models, 2024

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:47.146168Z

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Reference 52

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Reference 53

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Reference 54

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PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines Finetuned Language Models Are Zero-Shot Learners

Reference 55

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Reference 57

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Reference 59

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Reference 60

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Reference 61

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Reference 62

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Reference 64

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Reference 66

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Reference 69

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Reference 70

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Reference 71

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Reference 78

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Reference 80

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Reference 81

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Reference 82

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Reference 83

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Reference 84

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Reference 85

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Reference 86

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Reference 87

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Reference 88

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Reference 2020

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Reference 2023

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Reference 42

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Compliance-Scored Best-of-N Guardrail Orchestration for Multimodal Document Generation in Payments Dispute Defense cites this paper.

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Reference 22

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