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Paper Citation Record · LEDGER

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration

As of 15 August 2026, this Paper Citation Record lists 100 of 300 outbound references and 0 inbound Pith citation observations for arXiv:2608.07419.

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

pith.paper-citation-record.v1
2608.07419 v1

Coverage vector

measured 100 of 300 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:33:57.230753Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

100 of 300 outbound references displayed

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External citation measurements

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Outbound references

Observation af725466-9290-4f15-945c-c41b9016e78f · outbound

This paper cites Proceedings of the International Conference on Learning Representations (ICLR) , year=.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Proceedings of the International Conference on Learning Representations (ICLR) , year=

Reference 1

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This paper cites Proceedings of the Conference on Health, Inference, and Learning (CHIL) , year=.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Proceedings of the Conference on Health, Inference, and Learning (CHIL) , year=

Reference 2

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This paper cites Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) , year=.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) , year=

Reference 3

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This paper cites Advances in Neural Information Processing Systems , volume=.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Advances in Neural Information Processing Systems , volume=

Reference 4

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This paper cites International Conference on Artificial Intelligence and Statistics , pages=.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration International Conference on Artificial Intelligence and Statistics , pages=

Reference 5

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This paper cites See https://vicuna.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration See https://vicuna

Reference 6

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

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 7

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This paper cites Mistral 7B.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Mistral 7B

Reference 8

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Observation 6aa7d3ca-5bd3-46b9-943c-80de4e7e29f7 · outbound

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

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Gemma 2: Improving Open Language Models at a Practical Size

Reference 9

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration 2 OLMo 2 Furious

Reference 10

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This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 11

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration International conference on machine learning , pages=

Reference 12

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This paper cites Regularizing Neural Networks by Penalizing Confident Output Distributions.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Regularizing Neural Networks by Penalizing Confident Output Distributions

Reference 13

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This paper cites Proceedings of the IEEE conference on computer vision and pattern recognition , pages=.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

Reference 14

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Advances in Neural Information Processing Systems , volume=

Reference 15

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Advances in neural information processing systems , volume=

Reference 16

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Advances in neural information processing systems , volume=

Reference 17

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Crafting Papers on Machine Learning , year =

Reference 18

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Unresolved cited work

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Unresolved cited work

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration I , year =

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Unresolved cited work

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Unresolved cited work

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Newell and P

Reference 24

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Unresolved cited work

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This paper cites On divergences and informations in statistics and information theory , volume =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration On divergences and informations in statistics and information theory , volume =

Reference 26

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration doi:10.57967/hf/0181 , publisher =

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Simple statistical gradient-following algorithms for connectionist reinforcement learning , volume =

Reference 28

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration f -divergences , url =

Reference 29

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Convex analysis , volume =

Reference 30

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration PyTorch: An Imperative Style, High-Performance Deep Learning Library , url =

Reference 31

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Convex optimization , year =

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Convex analysis and minimization algorithms I: Fundamentals , volume =

Reference 33

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Information theory and statistics: A tutorial , volume =

Reference 34

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Entity-level Factual Consistency of Abstractive Text Summarization , url =

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration A Distributional Approach to Controlled Text Generation , url =

Reference 37

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Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration f -divergence Inequalities , volume =

Reference 38

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Observation 9e65c88f-31e1-42e8-8e03-e10c025ce026 · outbound

This paper cites Neural Text Generation from Structured Data with Application to the Biography Domain , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Neural Text Generation from Structured Data with Application to the Biography Domain , url =

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Observation 8fba8887-8540-44d4-9242-c5b402b1085c · outbound

This paper cites Controlling Conditional Language Models without Catastrophic Forgetting , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Controlling Conditional Language Models without Catastrophic Forgetting , url =

Reference 40

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source=arxiv_source observed=2026-08-15T14:33:56.944614Z digest=sha256:4658e082e021be45ec9b1b6af9221d03907be510f153aa457158fc67357d4380

Observation eb6ec6b5-8d69-485d-b95b-bb33af3f661f · outbound

This paper cites On f-divergences: Integral representations, local behavior, and inequalities , volume =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration On f-divergences: Integral representations, local behavior, and inequalities , volume =

Reference 41

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source=arxiv_source observed=2026-08-15T14:33:56.949430Z digest=sha256:cb566e8fbc5009942fed83c46f0f909730bf47707a752e4ef9c55e6584e3f14d

Observation 66500632-7ace-4fa9-8ba2-c43b7a3fa659 · outbound

This paper cites and Della Pietra, Stephen A.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration and Della Pietra, Stephen A

Reference 42

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source=arxiv_source observed=2026-08-15T14:33:56.955238Z digest=sha256:b43202e239159481cb0f1c6b44dd83cff79b8160dfdbb5bbb5b72d86d45a2177

Observation 99d027bb-6cfb-4939-9678-84defc74eedf · outbound

This paper cites Abstractive Text Summarization using Sequence-to-sequence.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Abstractive Text Summarization using Sequence-to-sequence

Reference 43

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source=arxiv_source observed=2026-08-15T14:33:56.961888Z digest=sha256:1bf0690b48763380798d3eafe36866d7f34225fdd6c3e2ed84de635f1cd05002

Observation 603b9ee9-fb85-4864-b3c2-dc8190b30522 · outbound

This paper cites Variational Inference with Tail-adaptive f-Divergence , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Variational Inference with Tail-adaptive f-Divergence , url =

Reference 44

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source=arxiv_source observed=2026-08-15T14:33:56.966741Z digest=sha256:85f486460e093c7e539cdfe21567d78cfcdce1a97a783be24fb762689fde8f0a

Observation cf5d80f1-5906-4b5c-8170-314ec6446b1d · outbound

This paper cites Transformers: State-of-the-Art Natural Language Processing , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Transformers: State-of-the-Art Natural Language Processing , url =

Reference 45

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source=arxiv_source observed=2026-08-15T14:33:56.971646Z digest=sha256:e35557f801e8167e92a57e50542cd4bd7f59987e4155262276b86e9e3034a783

Observation 6cd59760-014e-4013-ae40-108adc79d6fd · outbound

This paper cites Kingma and Jimmy Ba , bibsource =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Kingma and Jimmy Ba , bibsource =

Reference 46

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source=arxiv_source observed=2026-08-15T14:33:56.977001Z digest=sha256:40302e30aa016d5cab09aea2520ffae0eb4e4c3cb24969a409999e7ac8339a34

Observation 80df0a53-8348-45cd-9fbe-42b5eb8c674c · outbound

This paper cites Hierarchical Neural Story Generation , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Hierarchical Neural Story Generation , url =

Reference 47

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source=arxiv_source observed=2026-08-15T14:33:56.981700Z digest=sha256:3adbe1d3b9ffa7a777b7c35b0f75eebc95973fa6bda22bba23c887cd5acf6b17

Observation 1618b06e-e1ac-48ae-a4e6-b2b3f1a81c80 · outbound

This paper cites Courville and Yoshua Bengio , bibsource =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Courville and Yoshua Bengio , bibsource =

Reference 48

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source=arxiv_source observed=2026-08-15T14:33:56.986825Z digest=sha256:9dc487ec215eceba9ff012d01295c6e6dd3d43883de478d205280a1fc2e4dbc7

Observation 14272aab-278f-4891-bed9-604ade5b16e3 · outbound

This paper cites Reinforced Video Captioning with Entailment Rewards , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Reinforced Video Captioning with Entailment Rewards , url =

Reference 49

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doi, observed 2026-08-15T14:33:58.676702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-15T14:33:56.991526Z digest=sha256:5b6d797fc0c93af7165cf24139a74d508e932cd2397f90ca9a4a084baa38b944

Observation eeaa4754-cf5f-4569-b9b4-df93ea0ff529 · outbound

This paper cites Martin and Animesh Mehta and Brent Harrison and Mark O.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Martin and Animesh Mehta and Brent Harrison and Mark O

Reference 50

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source=arxiv_source observed=2026-08-15T14:33:56.996295Z digest=sha256:1910c0b22794e2b35c8975c739a57099c7a76a59abac76b78602b186844bf319

Observation e6de71c6-17ad-4c20-89a8-d350c81a0159 · outbound

This paper cites Deep Reinforcement Learning for Dialogue Generation , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Deep Reinforcement Learning for Dialogue Generation , url =

Reference 51

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source=arxiv_source observed=2026-08-15T14:33:57.001104Z digest=sha256:b7320701bb08b63051511fb26550190a1d9ca2e734b169048e88de956d64b853

Observation e0a22e36-0ccc-4ac1-9d23-80dfbd87db62 · outbound

This paper cites an unresolved cited work.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Unresolved cited work

Reference 52

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source=arxiv_source observed=2026-08-15T14:33:57.006306Z digest=sha256:5f7c683e24fe651607ba3aad42591f81a13e916dd0c9fd2cbca67d4e623045dc

Observation 8a37dd86-a5c6-4c04-baee-4a8d340099d1 · outbound

This paper cites The 22nd international conference on artificial intelligence and statistics , pages=.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration The 22nd international conference on artificial intelligence and statistics , pages=

Reference 53

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source=arxiv_source observed=2026-08-15T14:33:57.011934Z digest=sha256:8822be96275353024af997194af792f6081d350d61c31ae9a1443b07e2b811dd

Observation 795db5b7-8a60-445c-a8b8-e2eea2430f00 · outbound

This paper cites Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , pages=.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , pages=

Reference 54

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source=arxiv_source observed=2026-08-15T14:33:57.016719Z digest=sha256:2a5459afacad3dfa5c10f1fcdc57602f49f384a9b4266ce6be40c935af3eb73f

Observation 08af63a5-afd3-4df9-929e-a5a1a58285a7 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 55

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source=arxiv_source observed=2026-08-15T14:33:57.021187Z digest=sha256:58171cf789eaefbe1ef46141ac41b83243da6fd6570ed399b825cf8c9af5bab1

Observation f4d1b4f3-44fe-43d4-8591-9d5c5178ff37 · outbound

This paper cites Probabilistic model for code with decision trees , volume =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Probabilistic model for code with decision trees , volume =

Reference 56

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no resolver link, observed 2026-08-15T14:33:57.025622Z

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source=arxiv_source observed=2026-08-15T14:33:57.025622Z digest=sha256:ef4b4f19c6a05e7fd0b6b1f3a0b4ab63c93a67ec9bb71dbccd907f5f73d1701d

Observation 333c779c-0e91-42da-8d16-e4203a3d7c78 · outbound

This paper cites an unresolved cited work.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Unresolved cited work

Reference 57

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source=arxiv_source observed=2026-08-15T14:33:57.030193Z digest=sha256:0a10a722d547e42b3f4b293cea4a39d8dd080fd322f653672a44d03911e0feba

Observation 886d4348-b592-438e-83b1-3b0d9d7d65b0 · outbound

This paper cites The Woman Worked as a Babysitter: On Biases in Language Generation , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration The Woman Worked as a Babysitter: On Biases in Language Generation , url =

Reference 58

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source=arxiv_source observed=2026-08-15T14:33:57.035472Z digest=sha256:514e2e0b5263b71e9254019d095c1fb0cda93f20754b18b30bae372d56a9226b

Observation 77d41182-0132-48d8-b0f8-f9e096566577 · outbound

This paper cites On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting , url =

Reference 59

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source=arxiv_source observed=2026-08-15T14:33:57.040366Z digest=sha256:cb3aa3a0690af84dceeb1a1a73a4f0a4d6885778d9f24ce7caa3c3e4938707b3

Observation c59686d4-492c-4e00-a68d-64db87a13d28 · outbound

This paper cites and Daly, Raymond E.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration and Daly, Raymond E

Reference 60

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source=arxiv_source observed=2026-08-15T14:33:57.044750Z digest=sha256:ffc7c840555712e9d2c64002fe6905ed74abed7728a17b47e79d639e978695eb

Observation 515bfea2-c573-4a9d-b4dd-47fa65f60e5f · outbound

This paper cites A Diversity-Promoting Objective Function for Neural Conversation Models , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration A Diversity-Promoting Objective Function for Neural Conversation Models , url =

Reference 61

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source=arxiv_source observed=2026-08-15T14:33:57.049239Z digest=sha256:6fc3c6dace839246bc0fb4ad4c4debc7f938fc02d26dc12bd485b7e4d2d09d31

Observation 0097746b-eb61-4127-9a7d-5b600d90a76a · outbound

This paper cites Texygen:.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Texygen:

Reference 62

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source=arxiv_source observed=2026-08-15T14:33:57.054203Z digest=sha256:de8c937d4b85c10c09375109a2c667d91e28be15c977bb6b0f52b3ec9783f15a

Observation 377d0c24-1af0-48c5-9d36-ce216aacb8bc · outbound

This paper cites Language models are unsupervised multitask learners , volume =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Language models are unsupervised multitask learners , volume =

Reference 63

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source=arxiv_source observed=2026-08-15T14:33:57.058932Z digest=sha256:13f1c877008836ddf272133568bba35d23c00874c6118b8b6f6dcba384cb36b8

Observation e133c8c4-05ff-4a2f-8e70-5e3ee4cebae2 · outbound

This paper cites Fine-tuning language models from human preferences , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Fine-tuning language models from human preferences , url =

Reference 64

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source=arxiv_source observed=2026-08-15T14:33:57.063622Z digest=sha256:0f8b54c1816c05f1dc2490dc8b7ed271889be7afb4e5185e437bffd765a0d597

Observation 7b3eb115-6dc7-4076-ab9c-af3e90efe1ef · outbound

This paper cites A tutorial on energy-based learning , volume =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration A tutorial on energy-based learning , volume =

Reference 65

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source=arxiv_source observed=2026-08-15T14:33:57.068199Z digest=sha256:e33e06f7fe52efd824b6201cbe3db7a4bd36a596154f3f74f4b742a6014c8008

Observation d9ac53cb-1d7b-4373-bc8b-23b18acc9d09 · outbound

This paper cites Infinite-horizon policy-gradient estimation , volume =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Infinite-horizon policy-gradient estimation , volume =

Reference 66

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source=arxiv_source observed=2026-08-15T14:33:57.072749Z digest=sha256:939c9b58e5f3e6d4647c73c8c8c5b11d0c8a9cae2f10530737ded2bf706b709b

Observation f71c6baa-4521-4a2b-b438-6766908931be · outbound

This paper cites Training products of experts by minimizing contrastive divergence , volume =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Training products of experts by minimizing contrastive divergence , volume =

Reference 67

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source=arxiv_source observed=2026-08-15T14:33:57.077661Z digest=sha256:909e5e87da3a50222a757d5e8a999b0a1a179f442d4b289e65977fbb7f6bff93

Observation 31319ff9-598f-4265-b608-53e7e517caf1 · outbound

This paper cites Global Autoregressive Models for Data-Efficient Sequence Learning , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Global Autoregressive Models for Data-Efficient Sequence Learning , url =

Reference 68

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source=arxiv_source observed=2026-08-15T14:33:57.082525Z digest=sha256:31fbea2ae873988fd9819b5ee9f5f84c5be9d37d8614586062afb403b870dc8c

Observation cdd30e56-690e-451e-9f59-4f7d83b5bbfa · outbound

This paper cites Distributional reinforcement learning for energy-based sequential models , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Distributional reinforcement learning for energy-based sequential models , url =

Reference 69

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source=arxiv_source observed=2026-08-15T14:33:57.087122Z digest=sha256:3fec901fcc65402f0da44572f1660fafa12ceff41da133dbf426dcb8768c4c0d

Observation eb4ed9cf-7b0a-4607-801e-28ac78ec5b19 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Playing Atari with Deep Reinforcement Learning

Reference 70

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source=arxiv_source observed=2026-08-15T14:33:57.091820Z digest=sha256:9df96a12c1caf72b5258b6fade0f8b70454b45f0d33aed707e0cd7bda9a2f068

Observation c40c249d-cfcb-46f2-a26b-7b2a0e6cbb81 · outbound

This paper cites Jordan and Pieter Abbeel , bibsource =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Jordan and Pieter Abbeel , bibsource =

Reference 71

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source=arxiv_source observed=2026-08-15T14:33:57.096994Z digest=sha256:7d3bb0b29a6606dd17ab4751dd4af155e64ddb3c3e7cd8304297bb4eac46a9e0

Observation 1291f08b-1d02-4198-82be-ff4d2e265bd0 · outbound

This paper cites Proximal policy optimization algorithms , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Proximal policy optimization algorithms , url =

Reference 72

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no resolver link, observed 2026-08-15T14:33:57.101378Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T14:33:57.101378Z digest=sha256:1ce3a2f5f284e4e411194548ec1aeb1ebd85033fb6ac32da4c2682c4e07c94f1

Observation 4dc043b5-aa07-427b-9035-45c680e84baa · outbound

This paper cites Tuning recurrent neural networks with re-inforcement learning , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Tuning recurrent neural networks with re-inforcement learning , url =

Reference 73

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no resolver link, observed 2026-08-15T14:33:57.106144Z

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source=arxiv_source observed=2026-08-15T14:33:57.106144Z digest=sha256:afecb82e3b686d6671623fde5fd3cf055470298aa628fbbe5cb752b395bfef61

Observation 2653d197-cdc4-4909-bc17-f92f2dc3a9fe · outbound

This paper cites COLD decoding: Energy-based constrained text generation with langevin dynamics , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration COLD decoding: Energy-based constrained text generation with langevin dynamics , url =

Reference 74

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source=arxiv_source observed=2026-08-15T14:33:57.110689Z digest=sha256:8c21204e476bba921f21a7d4dde48eba40e0db07c4cb0f2c45ea66073320548e

Observation 52cfd967-72cf-4d9d-8f9c-5e7ad3e63526 · outbound

This paper cites An Explanation of In-context Learning as Implicit Bayesian Inference , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration An Explanation of In-context Learning as Implicit Bayesian Inference , url =

Reference 75

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source=arxiv_source observed=2026-08-15T14:33:57.115452Z digest=sha256:f1020e7c661da8c679a5a521e90d9bbbe072f899674840560717da3bad3353bc

Observation 04723fb0-6279-4275-9378-4a4ebf0c23c5 · outbound

This paper cites an unresolved cited work.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Unresolved cited work

Reference 76

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source=arxiv_source observed=2026-08-15T14:33:57.119906Z digest=sha256:98dd32f6446af11d5b2545d7502f7e3119b5db7be886a28782cbae1f78f24da4

Observation 8ba7f66a-6485-4d5e-a4f5-ce4d07460d7d · outbound

This paper cites RL with KL penalties is better viewed as Bayesian inference.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration RL with KL penalties is better viewed as Bayesian inference

Reference 77

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source=arxiv_source observed=2026-08-15T14:33:57.124460Z digest=sha256:f94519dcd7cb1a4fdf5505e4609439ad636d8fb60df6a409f9602518fdf861c8

Observation 46324ddc-457d-4a94-a6cb-c08b3c1545cd · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback , url =

Reference 78

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source=arxiv_source observed=2026-08-15T14:33:57.129118Z digest=sha256:990360e9f02375ea48f55c9e01eda784163bd800e3a42b89b19f93ac00285ad8

Observation 274a8131-8ca5-4873-97d8-a5f851c1341c · outbound

This paper cites and Lowe, Ryan and Voss, Chelsea and Radford, Alec and Amodei, Dario and Christiano, Paul , booktitle =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration and Lowe, Ryan and Voss, Chelsea and Radford, Alec and Amodei, Dario and Christiano, Paul , booktitle =

Reference 79

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source=arxiv_source observed=2026-08-15T14:33:57.133644Z digest=sha256:116efcddff79bfc57bcbb3c47eef86ce0190e86d9cb011e2ec9cde10b468acd0

Observation 5cd98524-5651-4f5c-b20e-0470ba91a435 · outbound

This paper cites Advances in neural information processing systems , volume=.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Advances in neural information processing systems , volume=

Reference 80

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source=arxiv_source observed=2026-08-15T14:33:57.138244Z digest=sha256:f7057a21d7f62d3ec23e08dd20e58b91102459ba226dcd5a216b03adec409936

Observation ed0213e2-728b-45f2-9c6f-feb18546326d · outbound

This paper cites Teaching language models to support answers with verified quotes , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Teaching language models to support answers with verified quotes , url =

Reference 81

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source=arxiv_source observed=2026-08-15T14:33:57.142652Z digest=sha256:cf41fc363a76f6b78630a136c392ea6fb75b0e179aeb0dae240cc66f3193e7f7

Observation 447b022d-ebf1-447f-b9d0-67d9c007791d · outbound

This paper cites Improving alignment of dialogue agents via targeted human judgements.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Improving alignment of dialogue agents via targeted human judgements

Reference 82

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source=arxiv_source observed=2026-08-15T14:33:57.147232Z digest=sha256:6df1027073bec049cd0478170868c607c8dad79504e10c130818b2c7befa16bc

Observation c6750c18-9717-4cd6-9017-2a56ab5ee40f · outbound

This paper cites Language model cascades , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Language model cascades , url =

Reference 83

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source=arxiv_source observed=2026-08-15T14:33:57.152934Z digest=sha256:30f78a23346bd32149c52c08f624c6f7a0e3259a848a0929ea0950afc68e329f

Observation 2346ffb3-ea28-4385-98e3-fc4183a5562f · outbound

This paper cites ArXiv preprint , title =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration ArXiv preprint , title =

Reference 84

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source=arxiv_source observed=2026-08-15T14:33:57.157481Z digest=sha256:c5a9c815c511e4ce7a2921a31b09e02f7a70af8e11de0f7f46b87a4944167bef

Observation a6b5686a-6d7d-441f-b384-af4db57aa04d · outbound

This paper cites Mescheder and Andreas Geiger and Sebastian Nowozin , bibsource =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Mescheder and Andreas Geiger and Sebastian Nowozin , bibsource =

Reference 85

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source=arxiv_source observed=2026-08-15T14:33:57.162101Z digest=sha256:5053a4130e1433280c4db902d9624ddd35acb778705c35382bff97a6db9426f3

Observation 46a11b83-bb53-4823-a71a-3129f3a874fc · outbound

This paper cites Generative Adversarial Networks , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Generative Adversarial Networks , url =

Reference 86

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source=arxiv_source observed=2026-08-15T14:33:57.166653Z digest=sha256:eff23b6e1eb83d4df55c7b7291cb353232d65ceae6fc459ed8ce0ed77ab4e55e

Observation 17ab093b-166c-4c7c-aa84-28534fd699ad · outbound

This paper cites Mode Regularized Generative Adversarial Networks , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Mode Regularized Generative Adversarial Networks , url =

Reference 87

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source=arxiv_source observed=2026-08-15T14:33:57.171515Z digest=sha256:86886f31dbb8f37e9d3e93c1791a9a4790df04c736331ffce42238b1cb37abf5

Observation 9ae8c800-8c61-413d-b077-603d2ea29f17 · outbound

This paper cites f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , url =

Reference 88

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source=arxiv_source observed=2026-08-15T14:33:57.176045Z digest=sha256:b9c6ee00f372945fda15b0a950541123fe6b3dda5a164f152b0473eb0c5d070c

Observation 2abc691a-e1cb-468f-9642-7471a6c57d6f · outbound

This paper cites Wasserstein Generative Adversarial Networks , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Wasserstein Generative Adversarial Networks , url =

Reference 89

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source=arxiv_source observed=2026-08-15T14:33:57.180704Z digest=sha256:ad452168069a7c79cb354c86eff251d7ee2d88861ec60e0b6dc26fbb232c1691

Observation ee9f2732-8899-4638-ad1c-7c7baa33d817 · outbound

This paper cites How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary? , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary? , url =

Reference 90

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source=arxiv_source observed=2026-08-15T14:33:57.185254Z digest=sha256:cb5439cc2f5c366af69cf930ffac313912483e31df51a96335a75a0ca4d52885

Observation 5f5c4e90-65cf-45b6-81e3-d0b0737cc5ed · outbound

This paper cites an unresolved cited work.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Unresolved cited work

Reference 91

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source=arxiv_source observed=2026-08-15T14:33:57.189823Z digest=sha256:66345c3b8194f16667dcbd9b8f9180cf0ac1c37d46f714faad4340ed16e7c42f

Observation 0e94129f-2d09-40c1-91d3-ea56dabd76a8 · outbound

This paper cites Scaling instruction-finetuned language models , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Scaling instruction-finetuned language models , url =

Reference 92

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source=arxiv_source observed=2026-08-15T14:33:57.194390Z digest=sha256:47c7bbf831d27d83e7043a211f9a3bc8ae51b954d2538b69056cffb372f22740

Observation 03b6dee3-3398-4b43-95a2-2fa4c56abcd5 · outbound

This paper cites Self-critiquing models for assisting human evaluators , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Self-critiquing models for assisting human evaluators , url =

Reference 93

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source=arxiv_source observed=2026-08-15T14:33:57.198820Z digest=sha256:853356333ea543666be49b6bb5a96d4a0a741b8f65c4c856bed41997b8a2cd86

Observation 38726201-e607-4654-9095-5e6a2b22fac3 · outbound

This paper cites Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets , url =

Reference 94

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source=arxiv_source observed=2026-08-15T14:33:57.203293Z digest=sha256:2537d934ca44ea66afbebbfa23975685fc017683430121fe5d13548f94ca518f

Observation 0e9db845-e824-4967-8fc2-09e2cf427a21 · outbound

This paper cites an unresolved cited work.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Unresolved cited work

Reference 95

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source=arxiv_source observed=2026-08-15T14:33:57.207858Z digest=sha256:41400501f599d1223b91da7da352f42a47454ef8c02922c5379fb14c62a5227c

Observation 901ed4b7-d47d-4fd5-88e2-1d332da4f728 · outbound

This paper cites Challenges in Detoxifying Language Models , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Challenges in Detoxifying Language Models , url =

Reference 96

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source=arxiv_source observed=2026-08-15T14:33:57.212359Z digest=sha256:a161eed5d42d71772f45e8370662120906d7655f126042c782ef8db787bea8fa

Observation 7938579b-100d-469d-8f68-19d8bfe62f8b · outbound

This paper cites ArXiv preprint , title =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration ArXiv preprint , title =

Reference 97

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source=arxiv_source observed=2026-08-15T14:33:57.216973Z digest=sha256:4005c4681aa2280c690f3eb47a97647147a458f51db08cfe850427e2d2c8e215

Observation fe9522e8-4ed0-4987-bae7-a27481bd400f · outbound

This paper cites ArXiv preprint , title =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration ArXiv preprint , title =

Reference 98

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source=arxiv_source observed=2026-08-15T14:33:57.221522Z digest=sha256:cf64c09a4ca50d4a96a6b88343257b624df6b06d8153ac4a40f8ad8df34552ac

Observation 9bfc43a6-2d05-458a-9ad5-40448b103c7e · outbound

This paper cites Owen , booktitle =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Owen , booktitle =

Reference 99

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source=arxiv_source observed=2026-08-15T14:33:57.226150Z digest=sha256:209cccacb37fa73a3c2c433a4ff9fe91eaf154b4ea4a6ffdd6516ede8899aa4f

Observation 2001f8b8-6601-4cbe-85b0-7c7cbac4970c · outbound

This paper cites Recipes for Building an Open-Domain Chatbot , url =.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Recipes for Building an Open-Domain Chatbot , url =

Reference 100

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source=arxiv_source observed=2026-08-15T14:33:57.230753Z digest=sha256:86b4f39dbc67c825559e080978d8bc0954bef4dce0c31f4c74c837ba41a48c92

Pith citing papers

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