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Paper Citation Record · LEDGER

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives

As of 12 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2412.10220.

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

pith.paper-citation-record.v1
2412.10220 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:17:07.599252Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T15:25:46.925378Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T10:36:02.611276Z

Reference resolution

47 of 47 outbound references displayed

  • verified exact3
  • verified fuzzy15
  • unresolved22
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 66e8265a-312d-4d50-bcfb-756138f8f99a · outbound

This paper cites an unresolved cited work.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Unresolved cited work

Reference 1

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Observation 9369f04f-3c22-4cf1-a56b-7868e9b8e011 · outbound

This paper cites an unresolved cited work.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Unresolved cited work

Reference 2

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Observation 100c07a3-073b-4e03-bfc9-2f220f0236f0 · outbound

This paper cites Flemish AI Research Program.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Flemish AI Research Program

Reference 3

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Observation cd76681c-7d66-48e2-87c5-d59b7ea23faf · outbound

This paper cites Lundberg and Su-In Lee.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Lundberg and Su-In Lee

Reference 4

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Source-reported events for the cited work

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Observation 066475d5-bf48-49a3-b03e-2192637f8faf · outbound

This paper cites ”why should i trust you?”: Explain- ing the predictions of any classifier.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives ”why should i trust you?”: Explain- ing the predictions of any classifier

Reference 5

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Observation b73734ac-5de6-45a1-b424-00d5f4ae1e04 · outbound

This paper cites A value for n-person games.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives A value for n-person games

Reference 6

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Observation 9ced43c6-22ad-4380-9140-5644f9125d11 · outbound

This paper cites The inadequacy of shapley values for explainabil- ity, 2023.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives The inadequacy of shapley values for explainabil- ity, 2023

Reference 7

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Observation 9ad79fb9-c267-417e-85d8-edbd5a8324eb · outbound

This paper cites Ex- plainability is not a game.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Ex- plainability is not a game

Reference 8

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Observation 49413a5f-6702-4cb8-91a9-8198cb441cc1 · outbound

This paper cites Natural language explanations for machine learning classification decisions.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Natural language explanations for machine learning classification decisions

Reference 9

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Observation deceb318-f251-4882-a7e4-c8a7c4ff4d12 · outbound

This paper cites Tell Me a Story! Narrative-Driven XAI with Large Language Models.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Tell Me a Story! Narrative-Driven XAI with Large Language Models

Reference 10

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Observation 524364df-b89a-4dfc-ac09-9230b5223d68 · outbound

This paper cites LLMs for XAI: Future Directions for Explaining Explanations.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives LLMs for XAI: Future Directions for Explaining Explanations

Reference 11

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Observation 50f871d1-740f-4857-ad95-1f1bcdb6c489 · outbound

This paper cites Natural Language Counterfactual Explanations for Graphs Using Large Language Models.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Natural Language Counterfactual Explanations for Graphs Using Large Language Models

Reference 12

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Observation 8add35a7-d315-4c68-a1c4-4c6cc4f2ab2d · outbound

This paper cites GraphNarrator: Generating Textual Explanations for Graph Neural Networks.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives GraphNarrator: Generating Textual Explanations for Graph Neural Networks

Reference 13

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Observation bcc5ea74-d2d2-4da0-9243-819377d8c44b · outbound

This paper cites GraphXAIN: Narratives to Explain Graph Neural Networks.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives GraphXAIN: Narratives to Explain Graph Neural Networks

Reference 14

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Observation 8d0861c0-51d3-4439-ac24-10a72a6582af · outbound

This paper cites Faithful and plausible natu- ral language explanations for image classifi- cation: A pipeline approach.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Faithful and plausible natu- ral language explanations for image classifi- cation: A pipeline approach

Reference 15

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Observation f65c85bd-728f-4e63-a4f9-bae8820dd64e · outbound

This paper cites In-Context Explainers: Harnessing LLMs for Explaining Black Box Models.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives In-Context Explainers: Harnessing LLMs for Explaining Black Box Models

Reference 16

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Observation 00ae3619-fd37-4e18-8a74-c1197510ebd9 · outbound

This paper cites Explaining ma- chine learning models with interactive natural language conversations using talktomodel.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Explaining ma- chine learning models with interactive natural language conversations using talktomodel

Reference 17

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Observation 80d6ab0b-8b0c-4aef-875f-12ceea63da2d · outbound

This paper cites ME- TEOR: An automatic metric for MT evalu- ation with improved correlation with human judgments.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives ME- TEOR: An automatic metric for MT evalu- ation with improved correlation with human judgments

Reference 18

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Observation 3460f186-b813-4886-88c3-85778c99a5fa · outbound

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Keane, Eoin M

Reference 19

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Observation 2fbded58-85d5-4a66-bd6a-bc8075889d01 · outbound

This paper cites Do models explain them- selves? Counterfactual simulatability of natural language explanations.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Do models explain them- selves? Counterfactual simulatability of natural language explanations

Reference 20

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Observation 70fdefc5-6b79-40cd-ac59-42c48798b14c · outbound

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Unresolved cited work

Reference 21

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Observation 88041425-3202-4a4e-b038-41cd32b37a42 · outbound

This paper cites BLEURT: Learning robust metrics for text generation.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives BLEURT: Learning robust metrics for text generation

Reference 22

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Observation 15b8aa88-629c-4543-ae6c-bbf0700e4d07 · outbound

This paper cites The disagreement problem in ex- plainable machine learning: A practitioner’s perspective.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives The disagreement problem in ex- plainable machine learning: A practitioner’s perspective

Reference 23

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Observation 1403399b-4080-476a-9e3f-8fcbddee5ac0 · outbound

This paper cites F ActScore: Fine-grained atomic evaluation of factual precision in long form text generation.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives F ActScore: Fine-grained atomic evaluation of factual precision in long form text generation

Reference 24

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Observation 6c835f72-4275-4d2e-b9b4-426295af1f3a · outbound

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives PRobELM: Plausibility ranking evaluation for language models

Reference 25

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Observation 24136dbe-fe04-4b8d-a2a1-a58fbf74c0d6 · outbound

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives A Survey on Natural Language Counterfactual Generation

Reference 26

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Observation 60bd9c9f-41c1-4e4d-898b-e54c2fdf6162 · outbound

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives You Can Generate It Again: Data-to-Text Generation with Verification and Correction Prompting

Reference 27

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This paper cites The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey

Reference 28

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Sentence- bert: Sentence embeddings using siamese bert-networks

Reference 29

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Towards few-shot fact-checking via perplexity

Reference 30

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives GPT-4 Technical Report

Reference 31

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Claude sonnet 3.5

Reference 32

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Perplexity from PLM Is Unreliable for Evaluating Text Quality

Reference 33

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Efficient Estimation of Word Representations in Vector Space

Reference 34

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Mistral large 2

Reference 35

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Zhang, Mark Har- man, and Meng Wang

Reference 36

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Observation fe4b8619-b339-47a3-b923-d630d7b01e23 · outbound

This paper cites NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Reference 37

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This paper cites Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts

Reference 38

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This paper cites Context-faithful prompting for large language models.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Context-faithful prompting for large language models

Reference 39

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Llama 3 model card

Reference 40

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives The llama 3 herd of mod- els, 2024

Reference 41

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Entity-based knowledge con- flicts in question answering

Reference 45

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives 19 org/CorpusID:201646309

Reference 2019

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives URL https: //doi.org/10.24963/ijcai.2021/609

Reference 2021

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives doi:10.1038/s42256- 023-00692-8

Reference 2023

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How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives Unresolved cited work

Reference 2024

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This paper cites URL https://huggingface.co/ mistralai/Mistral-Large-Instruct-2407.

How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives URL https://huggingface.co/ mistralai/Mistral-Large-Instruct-2407

Reference 2407

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Pith citing papers

Observation 3ecd87ac-c046-4939-811b-e477401c76f1 · inbound

A Two-Stage LLM Framework for Accessible and Verified XAI Explanations cites this paper.

A Two-Stage LLM Framework for Accessible and Verified XAI Explanations How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives

Reference 14

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Observation 94a37fcc-18ec-4d54-b785-7b9f871ab5e1 · inbound

On the Importance and Evaluation of Narrativity in Natural Language AI Explanations cites this paper.

On the Importance and Evaluation of Narrativity in Natural Language AI Explanations How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives

Reference 25

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