Paper Citation Record · LEDGER

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models

As of 4 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 1 inbound Pith citation observation for arXiv:2502.14427.

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

pith.paper-citation-record.v1
2502.14427 v2

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-23T02:51:16.495409Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T00:03:24.728318Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-03T00:16:12.290498Z

Reference resolution

67 of 67 outbound references displayed

verified exact44
verified fuzzy2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7bb52ec1-989c-4408-b350-8a5779017edb · outbound

This paper cites BMC Bioinformatics , volume =.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models BMC Bioinformatics , volume =

Reference 1

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.910745Z

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Observation a6dedb03-eec8-4dc7-95ff-87ecb4e6403f · outbound

This paper cites & Mitchell, T.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models & Mitchell, T

Reference 2

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.975572Z

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Observation f4ace10f-3980-45af-9bb5-c1fcdf6bfea3 · outbound

This paper cites Uncertainty in Natural Language Generation: From Theory to Applications.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Uncertainty in Natural Language Generation: From Theory to Applications

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:26.382148Z

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Observation a596585d-3bf9-42a7-928e-07ab7d5a988a · outbound

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Reference 4

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Observation ca59b282-c961-42f0-a3eb-337851f3a443 · outbound

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Reference 5

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Observation 8bae8d20-811b-4751-b54f-10263a41c271 · outbound

This paper cites an unresolved cited work.

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Reference 6

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Observation dd51c376-962e-4cd8-8077-126007bfcd5d · outbound

This paper cites Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:26.389425Z

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Observation 845a9e31-d18f-4973-b557-12f4af8b4e8c · outbound

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Reference 8

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Observation 273d690b-b2e3-473c-87eb-20f48f178c0e · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Training Verifiers to Solve Math Word Problems

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:26.385780Z

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Observation 65155ec8-83fb-42ac-a816-544d7f9d0e54 · outbound

This paper cites doi:10.18653/V1/2023.EMNLP-MAIN.357 , url =.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models doi:10.18653/V1/2023.EMNLP-MAIN.357 , url =

Reference 10

Resolution: verified exact; doi, observed 2026-05-23T02:52:26.051815Z

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Observation 9c8bdfa3-4355-4901-962a-dbe33a3eac09 · outbound

This paper cites Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models

Reference 11

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.965827Z

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Observation f3e2f7fe-a131-4b91-ba8b-e9df22a19eef · outbound

This paper cites The Llama 3 Herd of Models.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models The Llama 3 Herd of Models

Reference 12

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Observation 8fd75b3c-8513-4364-be18-c59d1b75ee22 · outbound

This paper cites On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?

Reference 13

Resolution: verified exact; doi, observed 2026-05-23T02:52:26.012957Z

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Observation e0c5699e-b1c9-49a7-868f-13f1a0afff05 · outbound

This paper cites Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:26.004352Z

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Observation 905a5499-2f73-4a42-851f-d0c4de97c938 · outbound

This paper cites LM-Polygraph: Uncertainty Estimation for Language Models.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models LM-Polygraph: Uncertainty Estimation for Language Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:26.036884Z

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Observation 4bc9dfe8-3ee5-4e57-b588-6990fcd2307f · outbound

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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Unresolved cited work

Reference 16

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Observation adb2a4e8-f4d6-4afe-ae55-dea679504bdd · outbound

This paper cites doi: 10.18653/v1/2024.acl-long.786.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models doi: 10.18653/v1/2024.acl-long.786

Reference 17

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.900472Z

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Observation ebb252b4-4b0c-4aec-b279-82c7302bb642 · outbound

This paper cites Unsupervised Quality Estimation for Neural Machine Translation.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Unsupervised Quality Estimation for Neural Machine Translation

Reference 18

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.932019Z

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Observation 79ec6dd3-8e46-4305-9bbe-e9b84476cd1a · outbound

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Reference 19

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Observation 9117c542-0aa7-450d-97bb-6c8af1b61cd0 · outbound

This paper cites A Survey of Con- fidence Estimation and Calibration in Large Language Models.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models A Survey of Con- fidence Estimation and Calibration in Large Language Models

Reference 20

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.947811Z

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Observation c2ba5e09-f861-4cf4-8149-b7f6d790abf8 · outbound

This paper cites SAMS um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models SAMS um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization

Reference 21

Resolution: verified exact; doi, observed 2026-05-23T02:52:26.040163Z

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Observation 75cbd88a-cbbf-482a-bd82-410708ad1498 · outbound

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Reference 22

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Reference 23

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Observation e8503a56-b523-4d34-8dd1-b2150e6ac946 · outbound

This paper cites InFindings of the Association for Computational Linguistics: ACL 2024, Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.).

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models InFindings of the Association for Computational Linguistics: ACL 2024, Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.)

Reference 24

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Observation ef9dcd8c-594a-47fc-838e-8c4295c84690 · outbound

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Reference 25

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Observation 7e2bb088-c009-4614-bedd-e27160a73ffe · outbound

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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Mistral 7B

Reference 26

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Observation 5212e125-269c-4fc1-b6e9-56c7c4b3ce38 · outbound

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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models PubMedQA: A Dataset for Biomedical Research Question Answering

Reference 27

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.935562Z

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Observation bfd3c8bd-74e7-4210-94a1-f7c7ea4ffe87 · outbound

This paper cites TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension

Reference 28

Resolution: verified exact; doi, observed 2026-05-23T02:52:26.016600Z

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Observation 136b92df-b032-46f2-9780-5ec49ba1fa84 · outbound

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This paper cites URLhttps://doi.org/10.18653/v1/2022.acl-long.229.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models URLhttps://doi.org/10.18653/v1/2022.acl-long.229

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This paper cites SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Reference 37

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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Proceedings of the 2023

Reference 38

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Observation 01870f0d-6f65-4b26-8b27-41b4d01177fe · outbound

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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Cohen, and Mirella Lapata

Reference 39

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.928145Z

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Observation 664d3f0f-b507-4bdb-935d-de18383cf89d · outbound

This paper cites Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:26.366839Z

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Observation 470d1ec5-d378-43cf-91b2-a4ee35e52d99 · outbound

This paper cites GPT-4 Technical Report.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models GPT-4 Technical Report

Reference 41

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Observation ec3d57a2-01ed-41f5-aee8-669f9810ff92 · outbound

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Reference 42

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Observation f0486f1f-70ee-48d3-a460-2b29e16783e8 · outbound

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Reference 43

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Observation d1c2204b-170a-43fd-8123-e394f7abe78e · outbound

This paper cites C o QA : A Conversational Question Answering Challenge.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models C o QA : A Conversational Question Answering Challenge

Reference 44

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.969300Z

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Observation 658b6f41-e555-406b-be5f-135810b269df · outbound

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Reference 45

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Observation e4fd7bea-0ee0-482d-9437-a4d96191688d · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Gemma 2: Improving Open Language Models at a Practical Size

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:25.988860Z

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Observation 4675b771-3f6e-46ee-93e8-23f67b1b0fc7 · outbound

This paper cites Journal of the American Statistical Association , author =.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Journal of the American Statistical Association , author =

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:25.994479Z

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Observation a0b04c96-c656-46ae-8281-89e9a3736224 · outbound

This paper cites Liu, and Christopher D.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Liu, and Christopher D

Reference 48

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.999082Z

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Observation 1f42a528-b0b7-416e-ab66-4eb53457d6e7 · outbound

This paper cites an unresolved cited work.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Unresolved cited work

Reference 49

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Observation 9c68f06f-b226-44d8-8371-ce88e350b108 · outbound

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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Unresolved cited work

Reference 50

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.938818Z

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This paper cites an unresolved cited work.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Unresolved cited work

Reference 51

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.958412Z

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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-05-23T02:52:27.445608Z

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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph

Reference 53

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This paper cites Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:26.371004Z

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Observation 5f2262c1-8f70-4428-97f9-d6795117580f · outbound

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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:26.378175Z

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Observation a35403ad-2345-46e6-aec4-da8370e4b6cc · outbound

This paper cites an unresolved cited work.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Unresolved cited work

Reference 56

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.950812Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2b0d654e-6962-47ce-88c9-21616de8c4c6 · outbound

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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-05-23T02:52:27.424649Z

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Observation 4aedb80e-9978-475c-a359-567a67c9e34c · outbound

This paper cites an unresolved cited work.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-05-23T02:52:27.418079Z

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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression

Reference 59

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.955430Z

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Observation 98dff816-7cbc-4b20-ae95-4cc85aa14499 · outbound

This paper cites Liu, and Matt Gardner.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Liu, and Matt Gardner

Reference 60

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.961740Z

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Observation 4df640e3-ce97-4e43-831d-d55123d56577 · outbound

This paper cites On hallucination and predictive uncertainty in conditional language generation.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models On hallucination and predictive uncertainty in conditional language generation

Reference 61

Resolution: verified exact; doi, observed 2026-05-23T02:52:26.032332Z

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Observation c80f7621-105d-4614-b0f9-40f86b164471 · outbound

This paper cites The art of abstention: Selective prediction and error regularization for natural language processing.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models The art of abstention: Selective prediction and error regularization for natural language processing

Reference 62

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.978743Z

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Observation f21e68f3-0214-49c2-a56e-098035943352 · outbound

This paper cites Detection of.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Detection of

Reference 63

Resolution: verified exact; doi, observed 2026-05-23T02:52:25.972577Z

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Observation bf858e54-3bbd-4d89-a33d-114ba94bd468 · outbound

This paper cites A lign S core: Evaluating Factual Consistency with A Unified Alignment Function.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models A lign S core: Evaluating Factual Consistency with A Unified Alignment Function

Reference 64

Resolution: verified exact; doi, observed 2026-05-23T02:52:26.049306Z

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Observation a91813c1-b701-453b-9cdf-39c331f0da5a · outbound

This paper cites an unresolved cited work.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Unresolved cited work

Reference 65

Resolution: verified exact; doi, observed 2026-05-23T02:52:26.043409Z

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Observation d5b5d1be-672b-4214-9eea-9c5316d0b895 · outbound

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Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models online" 'onlinestring :=

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:52:27.414405Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation abefa94a-7e3c-4be3-9356-d1d7dc676579 · outbound

This paper cites write newline.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models write newline

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:52:27.410184Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation dceb6651-06fb-4c06-84d8-20f2d4629987 · inbound

When Should LLMs Be Less Specific? Selective Abstraction for Reliable Long-Form Text Generation cites this paper.

When Should LLMs Be Less Specific? Selective Abstraction for Reliable Long-Form Text Generation Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-03T00:03:36.793586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.