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Paper Citation Record · LEDGER

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics

As of 14 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.00637.

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

pith.paper-citation-record.v1
2506.00637 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:07:31.149657Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

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  • verified fuzzy0
  • unresolved39
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation 1a7f804f-b208-4d6b-921f-ec86a1b30564 · outbound

This paper cites an unresolved cited work.

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

Reference 1

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Observation 96109b23-36c6-4a2b-a41e-dd7b1fa5927c · outbound

This paper cites an unresolved cited work.

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

Reference 2

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Observation 21c0f605-2577-4bf7-951d-24bfc4283785 · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Scaling Instruction-Finetuned Language Models

Reference 3

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This paper cites Logit-Based Ensemble Distribution Distillation for Robust Autoregressive Sequence Uncertainties.

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Logit-Based Ensemble Distribution Distillation for Robust Autoregressive Sequence Uncertainties

Reference 4

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Observation 511b36fd-5ad5-462b-b263-ee8f30f86034 · outbound

This paper cites Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience.

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience

Reference 5

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

Reference 6

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This paper cites Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models.

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models

Reference 7

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

Reference 8

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Observation 85d6a046-4c52-4cdd-9759-3c62ae25e2aa · outbound

This paper cites Large Language Models Must Be Taught to Know What They Don't Know.

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Large Language Models Must Be Taught to Know What They Don't Know

Reference 9

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This paper cites Abstractive Summarization of Reddit Posts with Multi-level Memory Networks.

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Abstractive Summarization of Reddit Posts with Multi-level Memory Networks

Reference 10

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

Reference 11

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This paper cites Calibration of Encoder Decoder Models for Neural Machine Translation.

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Calibration of Encoder Decoder Models for Neural Machine Translation

Reference 12

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This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 13

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This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 14

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

Reference 15

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

Reference 16

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

Reference 17

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness

Reference 18

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Uncertainty Estimation in Autoregressive Structured Prediction

Reference 19

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Ensemble Distribution Distillation

Reference 20

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Cohen, and Mirella Lapata

Reference 22

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities

Reference 23

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Bartezzaghi, Jasmina Bogojeska, Adelmo Cristiano Innocenza Malossi, and Thang Vu

Reference 29

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Liu, and Christopher D

Reference 30

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 33

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers

Reference 34

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs

Reference 35

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

Reference 36

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

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Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Unresolved cited work

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Observation 0b97b5c7-760d-48b4-90b2-40e8d77e6af3 · outbound

This paper cites URL: " 'urlintro :=.

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics URL: " 'urlintro :=

Reference 39

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no resolver link, observed 2026-08-07T12:07:31.102147Z

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source=arxiv_source observed=2026-08-07T12:07:31.102147Z digest=sha256:2a733851a2a5a85ef17c71a148e0fbfc8d55866ed63eae211bf59b8c04a20f5f

Observation 441f997a-136c-411f-968b-3adba89c35d7 · outbound

This paper cites write newline.

Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics write newline

Reference 40

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unresolved
no resolver link, observed 2026-08-07T12:07:31.149657Z

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source=arxiv_source observed=2026-08-07T12:07:31.149657Z digest=sha256:4938d627f0144f6a01fa7b5edfebce03c7478cb77ae12c86686cbccc19e682c8

Pith citing papers

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