GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models | Paper Citation Record

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This paper cites GPT-4 Technical Report.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models GPT-4 Technical Report

Reference 1

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Observation fdca9dc6-ff1a-4952-a999-6eb6eb42d824 · outbound

This paper cites Claude 3: A conversational ai model.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Claude 3: A conversational ai model

Reference 3

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This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 4

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This paper cites Large language models encode clinical knowledge.Nature, 620(7972):172–180.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Large language models encode clinical knowledge.Nature, 620(7972):172–180

Reference 5

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

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools

Reference 6

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This paper cites Finben: A holistic financial benchmark for large language models.Advances in Neural Information Processing Systems, 37:95716–95743.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Finben: A holistic financial benchmark for large language models.Advances in Neural Information Processing Systems, 37:95716–95743

Reference 7

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Observation e8cea93f-f185-4a6c-bec9-0ac77296fdcf · outbound

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

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Language Models (Mostly) Know What They Know

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-18T17:56:42.097881Z

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Observation 3782065e-5476-41b7-8f83-02116060f754 · outbound

This paper cites On calibration of modern neural networks.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models On calibration of modern neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.335401Z

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Observation c3d3c1c7-3b44-46d5-9250-a42e2e37e314 · outbound

This paper cites Calibration of pre-trained transformers.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Calibration of pre-trained transformers

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.359464Z

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This paper cites Large language models are miscalibrated in-context learners.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Large language models are miscalibrated in-context learners

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.339460Z

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Observation d076c677-f83b-4c5c-881a-3780e57bf923 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-18T17:56:42.118255Z

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Observation 0c2bcd3a-b37d-413d-b568-0eb15f60cbe9 · outbound

This paper cites Atomic calibration of llms in long-form generations.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Atomic calibration of llms in long-form generations

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-18T17:56:42.091914Z

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This paper cites Cali- brating large language models using their generations only.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Cali- brating large language models using their generations only

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.343511Z

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Observation 85e46730-e15d-473b-bbe1-eb47a51880a0 · outbound

This paper cites Large language models must be taught to know what they don’t know.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Large language models must be taught to know what they don’t know

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.326127Z

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Observation 97667c96-49ba-4fca-858f-39d8b2d0ba52 · outbound

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

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.317474Z

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Observation 4ffb4919-8d61-4eb2-b880-2a87652d84ba · outbound

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

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.292848Z

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This paper cites Linguistic calibration of long- form generations.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Linguistic calibration of long- form generations

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.285841Z

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Observation ed53c8f4-6f6f-4feb-9322-a9a4df42770d · outbound

This paper cites LoGU: Long-form Generation with Uncertainty Expressions.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models LoGU: Long-form Generation with Uncertainty Expressions

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-18T17:56:42.130120Z

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Observation 3cae4aef-f9af-420c-85f0-ad995bd3d50a · outbound

This paper cites Uncle: Uncertainty expressions in long-form generation.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Uncle: Uncertainty expressions in long-form generation

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-18T17:56:42.124226Z

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Observation 01baacb6-a6f0-45cb-b072-f6ec600633be · outbound

This paper cites Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T17:56:42.157411Z

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Observation a730ad1d-a6e4-40f7-8261-808fa776e6ea · outbound

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

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Lora: Low-rank adaptation of large language models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.275498Z

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Observation 89f3de43-2aa7-49b1-b27e-c74b2d8803b5 · outbound

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

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.271450Z

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Observation 7a177a63-dd6b-45f0-adc9-b552778b2af1 · outbound

This paper cites Get confused cautiously: Textual sequence memorization erasure with selective entropy maximization.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Get confused cautiously: Textual sequence memorization erasure with selective entropy maximization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.263942Z

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This paper cites Softmax probabilities (mostly) predict large language model correctness on multiple-choice q&a.arXiv e-prints, pages arXiv–2402.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Softmax probabilities (mostly) predict large language model correctness on multiple-choice q&a.arXiv e-prints, pages arXiv–2402

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.279253Z

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Observation 2190c1cd-728d-4d24-918d-0a2129f0b54f · outbound

This paper cites The internal state of an llm knows when it’s lying.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models The internal state of an llm knows when it’s lying

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.296344Z

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This paper cites Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-18T17:56:42.145469Z

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Observation 7d6f1316-593c-4071-aaea-2f799821bbaf · outbound

This paper cites Enhancing language model factuality via activation- based confidence calibration and guided decoding.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Enhancing language model factuality via activation- based confidence calibration and guided decoding

Reference 28

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Observation e9223cb5-e101-4aca-a683-a8ecf4eaaa04 · outbound

This paper cites arXiv preprint arXiv:2503.14749 , year=.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models arXiv preprint arXiv:2503.14749 , year=

Reference 29

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Observation 19bd4ae3-8f3a-441d-939a-2cde8c052cdd · outbound

This paper cites I don’t know: Explicit modeling of uncertainty with an [idk] token.Advances in Neural Information Processing Systems, 37:10935–10958.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models I don’t know: Explicit modeling of uncertainty with an [idk] token.Advances in Neural Information Processing Systems, 37:10935–10958

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.234081Z

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This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 31

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This paper cites Self-consistency improves chain of thought reasoning in language models.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Self-consistency improves chain of thought reasoning in language models

Reference 32

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This paper cites Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 33

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This paper cites s1: Simple test-time scaling.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models s1: Simple test-time scaling

Reference 34

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Observation b57c5af3-d79b-4191-aa1c-b425c288bd7e · outbound

This paper cites Escape sky-high cost: Early-stopping self-consistency for multi-step reasoning.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Escape sky-high cost: Early-stopping self-consistency for multi-step reasoning

Reference 35

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Observation 50d0baf7-7392-4ebf-a69b-f83d7a040033 · outbound

This paper cites Let’s sample step by step: Adaptive- consistency for efficient reasoning and coding with llms.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Let’s sample step by step: Adaptive- consistency for efficient reasoning and coding with llms

Reference 36

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Observation ee976613-51b5-4b71-8077-2c62e2cca600 · outbound

This paper cites Scaling Evaluation-time Compute with Reasoning Models as Evaluators.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Scaling Evaluation-time Compute with Reasoning Models as Evaluators

Reference 37

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Observation f3d21a7a-9e4e-4a70-a6ae-5db6ad9d8974 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems, 36:46595–46623.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems, 36:46595–46623

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.245509Z

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Observation c8716dbb-7c67-49c1-b1b2-41f33570ca58 · outbound

This paper cites Confidence improves self-consistency in llms.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Confidence improves self-consistency in llms

Reference 39

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Observation 1558b01b-1e7e-4872-b6a6-17c016193889 · outbound

This paper cites Efficient Test-Time Scaling via Self-Calibration.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Efficient Test-Time Scaling via Self-Calibration

Reference 40

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Observation 167cc81e-5d1d-452e-aded-20dbb3c36c63 · outbound

This paper cites Deep Think with Confidence.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Deep Think with Confidence

Reference 41

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Observation 7abfb6da-f762-4287-9bdf-6537a16da157 · outbound

This paper cites Think before you speak: Training language models with pause tokens.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Think before you speak: Training language models with pause tokens

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.229923Z

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Observation 579c00da-3e72-447f-97b4-af2bf2aeb0df · outbound

This paper cites Guiding language model reasoning with planning tokens.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Guiding language model reasoning with planning tokens

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.252859Z

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Observation 093ae9e3-4e72-4f5b-a013-a2241fe60813 · outbound

This paper cites Calibrated structured prediction.Advances in Neural Information Processing Systems, 28.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Calibrated structured prediction.Advances in Neural Information Processing Systems, 28

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.303661Z

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Observation 0ac20496-a55c-430d-bbb9-5c782734a6b9 · outbound

This paper cites Uncertainty estimation in autoregressive structured prediction.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Uncertainty estimation in autoregressive structured prediction

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.351910Z

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Observation ff9f386e-761a-4aa3-860e-b5f66c36f861 · outbound

This paper cites Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods.Advances in large margin classifiers, 10(3):61–74.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods.Advances in large margin classifiers, 10(3):61–74

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.260423Z

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Observation 80bc7247-a912-4607-8a3e-d70ca5d3a072 · outbound

This paper cites Reasoning aware self-consistency: Leveraging reasoning paths for efficient llm sampling.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Reasoning aware self-consistency: Leveraging reasoning paths for efficient llm sampling

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.256637Z

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Observation 0c875d73-d43f-4ab7-a834-60698ff28543 · outbound

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

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.282470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 96e64ac5-2b76-47a6-990c-f89ae605f7bf · outbound

This paper cites Crowdsourcing multiple choice science questions.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Crowdsourcing multiple choice science questions

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.289130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7ca5fbe7-7ef5-4fef-b421-71c07d77b853 · outbound

This paper cites Rouge: A package for automatic evaluation of summaries.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Rouge: A package for automatic evaluation of summaries

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.313162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 13b6c20a-52a8-48ef-95b7-ae89625033ae · outbound

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

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-18T17:56:42.178435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0c4109c0-f65a-4f1b-936c-3f23335492b2 · outbound

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

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-05-18T17:56:42.168290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 24c07ed8-69f7-4b0a-ad00-488385d64e2f · outbound

This paper cites The Llama 3 Herd of Models.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models The Llama 3 Herd of Models

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-18T17:56:42.173070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6f2fb485-c3aa-4696-a12b-0251b5050c98 · outbound

This paper cites Obtaining well calibrated probabilities using bayesian binning.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Obtaining well calibrated probabilities using bayesian binning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.321835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 80caeb5b-504d-48b7-8211-fb70a298da07 · outbound

This paper cites Verification of forecasts expressed in terms of probability.Monthly weather review, 78(1):1–3.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Verification of forecasts expressed in terms of probability.Monthly weather review, 78(1):1–3

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.308941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9829f9ac-1a30-47f1-bfbe-6fd7cfe361bd · outbound

This paper cites Qwen2.5 Technical Report.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Qwen2.5 Technical Report

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-05-18T17:56:42.140456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5a7a77d9-755d-4bfa-b0fa-147e5c5da30e · outbound

This paper cites Mathqa: Towards interpretable math word problem solving with operation-based formalisms.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Mathqa: Towards interpretable math word problem solving with operation-based formalisms

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T17:56:43.300307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2b7dbcc3-2b18-46a2-bed1-30c1e8c74888 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-05-18T17:56:42.163103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f7e4c79b-102c-444a-a876-cb7428721ea2 · outbound

This paper cites Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs.

GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs

Reference 59

Resolution: malformed identifier; arxiv_id, observed 2026-05-18T17:56:42.135178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers