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Paper Citation Record · LEDGER

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2501.03991.

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

pith.paper-citation-record.v1
2501.03991 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:45:55.440332Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T03:10:22.314719Z

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

38 of 38 outbound references displayed

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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 2437d2a3-1f25-4f30-8035-dab3d49d55f2 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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Observation d4accea8-d40d-4512-a807-6766d7f8e59a · outbound

This paper cites an unresolved cited work.

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Unresolved cited work

Reference 2

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Observation 6fe7c2af-1b1f-4142-8d55-05f0dbd5efbf · outbound

This paper cites Unleashing the potential of prompt engineering for large language models.

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Unleashing the potential of prompt engineering for large language models

Reference 3

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Observation 9655f459-5a0d-4bc2-8e71-fcf3c3814996 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 4

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This paper cites an unresolved cited work.

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Unresolved cited work

Reference 5

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This paper cites an unresolved cited work.

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Unresolved cited work

Reference 6

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Observation 363c05d4-b30c-44fe-90be-f9457651271b · outbound

This paper cites The Llama 3 Herd of Models.

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles The Llama 3 Herd of Models

Reference 7

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This paper cites Weinberger.

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Weinberger

Reference 8

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Reference 9

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This paper cites Mixtral of Experts.

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Mixtral of Experts

Reference 10

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Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Unresolved cited work

Reference 11

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Observation b6fd4399-7601-495f-87fb-6ccacd27f204 · outbound

This paper cites Crowdsourcing Multiple Choice Science Questions.

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Crowdsourcing Multiple Choice Science Questions

Reference 12

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Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Unresolved cited work

Reference 13

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Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Unresolved cited work

Reference 14

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

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models

Reference 15

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Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Unresolved cited work

Reference 16

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Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Unresolved cited work

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Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles LitCab: Lightweight Language Model Calibration over Short- and Long-form Responses

Reference 19

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Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Are Large Language Models More Honest in Their Probabilistic or Verbalized Confidence?

Reference 23

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Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Gemma 2: Improving Open Language Models at a Practical Size

Reference 26

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Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Llama 2: Open Foundation and Fine-Tuned Chat Models

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Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs

Reference 32

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Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Qwen2 Technical Report

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Resolution
unresolved
no resolver link, observed 2026-08-10T21:45:55.440332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T21:45:55.440332Z digest=sha256:d39e1030f2d4fb459222acea8584cb83fe64af11f5a316ada6dbc87a1e8decb4

Pith citing papers

Observation e23a4143-c4fd-4ba1-9487-e5d342c18bc6 · inbound

Diversity in Large Language Models under Supervised Fine-Tuning cites this paper.

Diversity in Large Language Models under Supervised Fine-Tuning Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles

Reference 70

Resolution
verified exact
arxiv_id, observed 2026-05-09T20:37:32.184304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-09T20:32:37.788283Z digest=sha256:088fa0c295f63bf07e46af9377bbbcc60c32fcd3e3a8781849fe3e1c3dc488a8

Observation 1c83a074-fcde-4da3-b10c-d6ad65543441 · inbound

Diversity in Large Language Models under Supervised Fine-Tuning cites this paper.

Diversity in Large Language Models under Supervised Fine-Tuning Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles

Reference 70

Resolution
verified exact
arxiv_id, observed 2026-05-12T03:11:18.163273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-12T03:10:22.314719Z digest=sha256:d6a980a70087bef93cdf81501a76fbe6c40dec852eb2dfdba5b47612c5903cff