Paper Citation Record · LEDGER

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning

As of 14 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 3 inbound Pith citation observations for arXiv:2412.02904.

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

pith.paper-citation-record.v1
2412.02904 v2

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

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measured 73 of 73 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:23:05.868780Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T12:46:04.140262Z

Reference resolution

70 of 70 outbound references displayed

verified exact10
verified fuzzy57
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8c4f40ff-de33-4af1-a60e-a61e6361b3cc · outbound

This paper cites A review of uncertainty quantification in deep learning: Techniques, applications and challenges.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Reference 1

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Observation e817c20a-a86b-4df2-a03c-3e50cc15e119 · outbound

This paper cites GPT-4 Technical Report.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning GPT-4 Technical Report

Reference 2

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Observation 779c328c-30d0-43a4-8ef6-dea6821ee3e0 · outbound

This paper cites Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AI.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AI

Reference 3

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Observation 7afae257-d208-4360-bbf4-eb56f0c69f5a · outbound

This paper cites Concrete Problems in AI Safety.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Concrete Problems in AI Safety

Reference 4

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Observation 076c90fa-944c-4cdb-b4a1-a3be2a4ffd4e · outbound

This paper cites Linguistic calibration of long-form generations.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Linguistic calibration of long-form generations

Reference 5

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Observation 6b0900e5-fe8a-4d70-9209-9f73205615e9 · outbound

This paper cites Pearson correlation coefficient.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Pearson correlation coefficient

Reference 6

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Observation f3e340c0-b8fd-4cd2-98a3-cd63015bf5fd · outbound

This paper cites Confabulations: a conceptual history.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Confabulations: a conceptual history

Reference 7

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Observation dac8ab62-1a26-4aac-85c7-a8a2223b1df3 · outbound

This paper cites Weight uncertainty in neural network.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Weight uncertainty in neural network

Reference 8

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Observation 45b460ca-fc65-4bd5-adad-9e587d0d9482 · outbound

This paper cites The relationship between precision-recall and roc curves.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning The relationship between precision-recall and roc curves

Reference 9

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Observation d26363ca-b7a3-40ec-935c-4fbe59399978 · outbound

This paper cites Calibration of pre-trained transformers.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Calibration of pre-trained transformers

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.925729Z

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Observation 694fd0d0-24a1-46ef-a580-9355a194defc · outbound

This paper cites Determinants of LLM-assisted Decision-Making.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Determinants of LLM-assisted Decision-Making

Reference 11

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Observation 4489f1a7-cf65-40b5-b2b3-6c04ce7dd3c3 · outbound

This paper cites Lm-polygraph: Uncertainty estimation for language models.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Lm-polygraph: Uncertainty estimation for language models

Reference 12

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Observation 70e07ac5-f5b5-431a-97a7-bf241f7a32a5 · outbound

This paper cites Detecting hallucinations in large language models using semantic entropy.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Detecting hallucinations in large language models using semantic entropy

Reference 13

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Observation 5dc5cacf-ae59-4055-8b2f-f4f5de16286d · outbound

This paper cites Unsupervised quality estimation for neural machine translation.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Unsupervised quality estimation for neural machine translation

Reference 14

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Observation 83dd29a2-a3d7-4513-a78f-d562bf3d5a1d · outbound

This paper cites A survey of uncertainty in deep neural networks.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning A survey of uncertainty in deep neural networks

Reference 15

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Observation 05974c82-d544-4f9a-b2d9-b84648e543fc · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Gemma: Open Models Based on Gemini Research and Technology

Reference 16

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Observation 0a71d798-363c-477a-b247-c28367584a7c · outbound

This paper cites A survey of confidence estimation and calibration in large language models.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning A survey of confidence estimation and calibration in large language models

Reference 17

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Observation e0932636-c310-4de1-af9f-19454a273bd9 · outbound

This paper cites On calibration of modern neural networks.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning On calibration of modern neural networks

Reference 18

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Observation fd07a85a-214c-480b-bc6c-c7d3351df6f8 · outbound

This paper cites Augmix: A simple data processing method to improve robustness and uncertainty.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Augmix: A simple data processing method to improve robustness and uncertainty

Reference 19

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Observation 10ab343f-4110-4af6-89ec-37b59d9c2d50 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Parameter-efficient transfer learning for nlp

Reference 20

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Observation 4132f4e0-5b84-4861-bf71-f8a995a96ad0 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Lora: Low-rank adaptation of large language models

Reference 21

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This paper cites Survey of hallucination in natural language generation.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Survey of hallucination in natural language generation

Reference 22

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Observation c577868f-6fb5-4ebd-b735-ff5c1f722986 · outbound

This paper cites How can we know when language models know? on the calibration of language models for question answering.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning How can we know when language models know? on the calibration of language models for question answering

Reference 23

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

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension

Reference 24

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Observation b9983758-f1d1-4641-8a62-7b8a2c408be0 · outbound

This paper cites Language Models (Mostly) Know What They Know.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Language Models (Mostly) Know What They Know

Reference 25

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Observation 8bed5dcf-7e58-4994-9835-0be5ce409ef7 · outbound

This paper cites Calibration-tuning: Teaching large language models to know what they don’t know.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Calibration-tuning: Teaching large language models to know what they don’t know

Reference 26

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Observation fcfc0f39-8cbf-4679-a886-13fcdeea985c · outbound

This paper cites Soft calibration objectives for neural networks.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Soft calibration objectives for neural networks

Reference 27

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Observation 45089ce1-bd8e-4b43-96ab-e376028db727 · outbound

This paper cites Calibrated language model fine-tuning for in-and out-of-distribution data.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Calibrated language model fine-tuning for in-and out-of-distribution data

Reference 28

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Observation 3508bfd4-1175-4ef1-ba2b-079634e7e76b · outbound

This paper cites Improving model calibration with accuracy versus uncertainty optimization.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Improving model calibration with accuracy versus uncertainty optimization

Reference 29

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This paper cites Bioasq-qa: A manually curated corpus for biomedical question answering.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Bioasq-qa: A manually curated corpus for biomedical question answering

Reference 30

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Observation 19478475-987a-4e32-a2d2-d0a3c9434a3c · outbound

This paper cites Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation

Reference 31

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This paper cites Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers

Reference 32

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This paper cites Trainable calibration measures for neural networks from kernel mean embeddings.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Trainable calibration measures for neural networks from kernel mean embeddings

Reference 33

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This paper cites Conformal Prediction with Large Language Models for Multi-Choice Question Answering.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Conformal Prediction with Large Language Models for Multi-Choice Question Answering

Reference 34

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This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 35

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This paper cites Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics

Reference 36

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This paper cites Generating with confidence: Uncertainty quantification for black-box large language models.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Generating with confidence: Uncertainty quantification for black-box large language models

Reference 37

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Observation 459c237d-032c-4dc2-bb24-5dfeb858db34 · outbound

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Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Visual instruction tuning

Reference 38

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Observation be7cd463-cc69-4619-9237-7010bf144457 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.807731Z

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This paper cites Litcab: Lightweight language model calibration over short-and long-form responses.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Litcab: Lightweight language model calibration over short-and long-form responses

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.815311Z

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Observation f921f4d4-fb0c-4c0e-8def-cdbc819af81f · outbound

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Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Decoupled weight decay regularization

Reference 41

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Observation 8a2568bd-c6e4-4cd4-a887-6594def60e28 · outbound

This paper cites Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools

Reference 42

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This paper cites Uncertainty estimation in autoregressive structured prediction.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Uncertainty estimation in autoregressive structured prediction

Reference 43

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Observation c7fc8831-d908-48c2-abca-a9ea00cd375b · outbound

This paper cites Peft: State-of-the-art parameter-efficient fine-tuning methods.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Peft: State-of-the-art parameter-efficient fine-tuning methods

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.957522Z

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Observation 897308b0-fef8-47c8-82f6-ffc94b6648fa · outbound

This paper cites Ok-vqa: A visual question answering benchmark requiring external knowledge.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Ok-vqa: A visual question answering benchmark requiring external knowledge

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.818902Z

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Observation 24fbf246-66ee-461f-8b6e-6eae1ed53e81 · outbound

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

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Factscore: Fine-grained atomic evaluation of factual precision in long form text generation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.811861Z

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Observation ab9dfdb4-0095-4e4f-b730-7d0d6e69c33d · outbound

This paper cites Revisiting the calibration of modern neural networks.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Revisiting the calibration of modern neural networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.857572Z

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Observation a90da544-c2de-4192-aef7-0994e5433282 · outbound

This paper cites Calibrating deep neural networks using focal loss.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Calibrating deep neural networks using focal loss

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:43.015012Z

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Observation 76f39cf0-a24c-4872-841e-60042e14ea6b · outbound

This paper cites Machine learning: a probabilistic perspective.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Machine learning: a probabilistic perspective

Reference 49

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This paper cites Accuracy-rejection curves (arcs) for comparing classification methods with a reject option.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Accuracy-rejection curves (arcs) for comparing classification methods with a reject option

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.982499Z

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This paper cites Obtaining well calibrated probabilities using bayesian binning.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Obtaining well calibrated probabilities using bayesian binning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.921704Z

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This paper cites Measuring calibration in deep learning.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Measuring calibration in deep learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.888223Z

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This paper cites Probabilities of Chat LLMs Are Miscalibrated but Still Predict Correctness on Multiple-Choice Q&A.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Probabilities of Chat LLMs Are Miscalibrated but Still Predict Correctness on Multiple-Choice Q&A

Reference 53

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This paper cites Coqa: A conversational question answering challenge.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Coqa: A conversational question answering challenge

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.972210Z

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This paper cites Out-of-distribution detection and selective generation for conditional language models.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Out-of-distribution detection and selective generation for conditional language models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.832839Z

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This paper cites The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:43.006293Z

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Observation 6dd8507a-d879-4fce-bfe9-d20e11192cbb · outbound

This paper cites On mixup training: Improved calibration and predictive uncertainty for deep neural networks.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning On mixup training: Improved calibration and predictive uncertainty for deep neural networks

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.891592Z

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Observation 9a9768ce-77a1-4771-a94e-c8191cd2f8d3 · outbound

This paper cites Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.910693Z

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Observation b25f17c9-06c5-46e8-a362-f45128d13b52 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-05-23T07:47:42.662943Z

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Observation 9db33b92-2900-4732-8f06-6a16f165a1d5 · outbound

This paper cites Adamix: Mixture-of-adaptations for parameter-efficient model tuning.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Adamix: Mixture-of-adaptations for parameter-efficient model tuning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.968779Z

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Observation 752b5267-276f-4f46-904c-a8e5d336caa8 · outbound

This paper cites Neural text generation with unlikelihood training.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Neural text generation with unlikelihood training

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.943209Z

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Observation ea1185c7-c458-41d2-838e-d3d340ffbfe2 · outbound

This paper cites Quantifying uncertainties in natural language processing tasks.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Quantifying uncertainties in natural language processing tasks

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.850327Z

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Observation 36d95b7a-b4c1-436e-869e-86ad2ebfc1ef · outbound

This paper cites Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.961304Z

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Observation 42eba806-e371-41b0-9bcf-25f615400423 · outbound

This paper cites Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-23T07:47:42.601393Z

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Observation d69cb2ac-4030-4122-a66a-8406913022de · outbound

This paper cites Bertscore: Evaluating text generation with bert.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Bertscore: Evaluating text generation with bert

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.929548Z

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Observation 09781487-70cc-4f3b-acb4-2ce3bc08bf83 · outbound

This paper cites CRC standard probability and statistics tables and formulae.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning CRC standard probability and statistics tables and formulae

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.878265Z

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Observation 735f14fd-96f6-4c0e-b7d0-155f34dc5ad6 · outbound

This paper cites write newline.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning write newline

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.950372Z

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Observation 6766b570-8f6b-471f-a352-9086ce0c387c · outbound

This paper cites @esa (Ref.

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning @esa (Ref

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T07:47:42.907147Z

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Observation 7a866179-724f-43ba-a033-1cc7b247e640 · outbound

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Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Unresolved cited work

Reference 69

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Observation a99d04e2-26f9-4a12-b171-7c2b4e881fd7 · outbound

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Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-05-23T07:47:42.899422Z

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Pith citing papers

Observation 35bffa0d-f7a6-4747-b753-572bcd851601 · inbound

Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models cites this paper.

Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning

Reference 40

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Observation 006c82c8-55b7-406e-9150-46b26404a987 · inbound

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration cites this paper.

Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:46:04.143047Z

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Observation 7403e69b-0c10-4ef0-b6e8-2d001ad2ff57 · inbound

Reliability Scaling Laws for Quantized Large Language Models cites this paper.

Reliability Scaling Laws for Quantized Large Language Models Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning

Reference 104

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