Paper Citation Record · LEDGER

Quantifying perturbation impacts for large language models

As of 13 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 5 inbound Pith citation observations for arXiv:2412.00868.

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

pith.paper-citation-record.v1
2412.00868 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:59:20.591451Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:45:11.743489Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

26 of 26 outbound references displayed

verified exact0
verified fuzzy9
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation a5b4dcab-107e-423a-8815-5a63eea81f1b · outbound

This paper cites The Effect of Sampling Temperature on Problem Solving in Large Language Models.

Quantifying perturbation impacts for large language models The Effect of Sampling Temperature on Problem Solving in Large Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.465435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0480f203-c212-4846-9075-e2805bc47d9b · outbound

This paper cites How resilient are language models to text perturbations? In International Conference on Intelligent Data Engineering and Automated Learning, pages 85–96.

Quantifying perturbation impacts for large language models How resilient are language models to text perturbations? In International Conference on Intelligent Data Engineering and Automated Learning, pages 85–96

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:59:21.016153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 828f1885-8d36-4827-9f6b-b8f57275a739 · outbound

This paper cites The imperative for regulatory oversight of large language models (or generative ai) in healthcare.

Quantifying perturbation impacts for large language models The imperative for regulatory oversight of large language models (or generative ai) in healthcare

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:59:20.999554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 61fdbd33-a636-49b4-be88-21e5a4ed83d8 · outbound

This paper cites Chatgpt and the ai act.

Quantifying perturbation impacts for large language models Chatgpt and the ai act

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:59:20.983167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2ddc8c27-367c-4208-9921-7a24dfcf910b · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Quantifying perturbation impacts for large language models Explaining and Harnessing Adversarial Examples

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.486513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 842b93fe-5105-46bb-94bc-b94b00ad396f · outbound

This paper cites why should i trust you?.

Quantifying perturbation impacts for large language models why should i trust you?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.492159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92488073-9f88-45c5-97b7-4a6e67bde2e0 · outbound

This paper cites Accountability of AI Under the Law: The Role of Explanation.

Quantifying perturbation impacts for large language models Accountability of AI Under the Law: The Role of Explanation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.497463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 698128f1-e3dc-4160-bf16-bf63900f9e8c · outbound

This paper cites Re-evaluating Evaluation in Text Summarization.

Quantifying perturbation impacts for large language models Re-evaluating Evaluation in Text Summarization

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.502857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2df7d1fc-b1a5-4781-ac15-2af8de3c178c · outbound

This paper cites Rouge: A package for automatic evaluation of summaries.

Quantifying perturbation impacts for large language models Rouge: A package for automatic evaluation of summaries

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.507559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e95f6948-078a-43c2-90d5-f36d6622c78b · outbound

This paper cites Counter- factual fairness in text classification through robustness.

Quantifying perturbation impacts for large language models Counter- factual fairness in text classification through robustness

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:59:20.945020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ddf7d0c8-f4b7-45d0-b591-5056e8cc7eb3 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Quantifying perturbation impacts for large language models ReAct: Synergizing Reasoning and Acting in Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.517714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91e6f590-517f-4f7e-9236-68131aab5aca · outbound

This paper cites Reasoning with Language Model is Planning with World Model.

Quantifying perturbation impacts for large language models Reasoning with Language Model is Planning with World Model

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.522829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 258a46f4-2eb7-4f2b-8f36-041d71664698 · outbound

This paper cites Resampling methods: concepts, applications, and justification.

Quantifying perturbation impacts for large language models Resampling methods: concepts, applications, and justification

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:59:20.928034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 94eed61d-9300-40ad-86bc-d7a926132ccc · outbound

This paper cites Nuanced metrics for measuring unintended bias with real data for text classification.

Quantifying perturbation impacts for large language models Nuanced metrics for measuring unintended bias with real data for text classification

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:59:20.913062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8e3cfc4d-032a-44dd-9a42-ebd89dee0fb4 · outbound

This paper cites Measuring and mitigating unintended bias in text classification.

Quantifying perturbation impacts for large language models Measuring and mitigating unintended bias in text classification

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:59:20.897236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6716d440-1f11-45b7-885d-07afba74b016 · outbound

This paper cites Reducing Gender Bias in Abusive Language Detection.

Quantifying perturbation impacts for large language models Reducing Gender Bias in Abusive Language Detection

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.541829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4086d64-494b-447f-84bb-4b7f26eef699 · outbound

This paper cites The (im) possibility of fairness: Different value systems require different mechanisms for fair decision making.

Quantifying perturbation impacts for large language models The (im) possibility of fairness: Different value systems require different mechanisms for fair decision making

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:59:20.879467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f8dd21e5-9209-465f-9b1f-ec6c732d19fb · outbound

This paper cites Inherent Trade-Offs in the Fair Determination of Risk Scores.

Quantifying perturbation impacts for large language models Inherent Trade-Offs in the Fair Determination of Risk Scores

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.551413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 913e8ca7-9aed-4f9e-8c38-b2ad2edcb33c · outbound

This paper cites The cost of fairness in binary classification.

Quantifying perturbation impacts for large language models The cost of fairness in binary classification

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.556009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5eeef890-376b-419b-8838-5c14e66bd286 · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Quantifying perturbation impacts for large language models BERTScore: Evaluating Text Generation with BERT

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.561567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f82b4d04-1a3a-4d87-aa8e-c80bd08ffc3f · outbound

This paper cites MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance.

Quantifying perturbation impacts for large language models MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.566022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 029ff3ae-5c36-4b66-969c-709391015265 · outbound

This paper cites The Rise and Potential of Large Language Model Based Agents: A Survey.

Quantifying perturbation impacts for large language models The Rise and Potential of Large Language Model Based Agents: A Survey

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.571829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e13cdb1d-ef27-43b8-bc97-51d68b7c14c0 · outbound

This paper cites Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models.

Quantifying perturbation impacts for large language models Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.576565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd6fe573-6e0a-4c9e-9c4e-b144e9551bc8 · outbound

This paper cites Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments.

Quantifying perturbation impacts for large language models Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.582170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 573d5d9d-0e8a-40d8-bae9-55a8d5dc19e2 · outbound

This paper cites Partially observable cost-aware active-learning with large language models.

Quantifying perturbation impacts for large language models Partially observable cost-aware active-learning with large language models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:59:20.852353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 61721f3e-6358-4e00-b2a4-8c6a90be8dcd · outbound

This paper cites Large Language Models to Enhance Bayesian Optimization.

Quantifying perturbation impacts for large language models Large Language Models to Enhance Bayesian Optimization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T04:59:20.591451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 97cac6cc-4003-485c-973a-6f381383f10f · inbound

Large Language Models for Statistical Inference: Context Augmentation with Applications to the Two-Sample Problem and Regression cites this paper.

Large Language Models for Statistical Inference: Context Augmentation with Applications to the Two-Sample Problem and Regression Quantifying perturbation impacts for large language models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:11.743489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 806f548e-a973-408f-a635-6ee93f4739c4 · inbound

"GenAI Defaults to Bias!" Gamify AI Literacy Through Reflections on Prompts cites this paper.

"GenAI Defaults to Bias!" Gamify AI Literacy Through Reflections on Prompts Quantifying perturbation impacts for large language models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-04T16:29:40.984617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ecf9eab-3204-4f64-b082-584c998788ba · inbound

Evaluating Reliability Gaps in Large Language Model Safety via Repeated Prompt Sampling cites this paper.

Evaluating Reliability Gaps in Large Language Model Safety via Repeated Prompt Sampling Quantifying perturbation impacts for large language models

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-15T12:55:37.929010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 827de993-6f2e-4110-80ac-e38e3ba56e4e · inbound

Compared to What? Baselines and Metrics for Counterfactual Prompting cites this paper.

Compared to What? Baselines and Metrics for Counterfactual Prompting Quantifying perturbation impacts for large language models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-09T19:05:10.628132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 56ae1548-4fc0-4783-a2a7-e6eb6fc9fc12 · inbound

Consistency as a Testable Property: Statistical Methods to Evaluate AI Agent Reliability cites this paper.

Consistency as a Testable Property: Statistical Methods to Evaluate AI Agent Reliability Quantifying perturbation impacts for large language models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:41:21.807858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.