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Paper Citation Record · LEDGER

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations

As of 7 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 0 inbound Pith citation observations for arXiv:2506.02696.

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

pith.paper-citation-record.v1
2506.02696 v1

Coverage vector

measured 78 of 78 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:23:47.566632Z

measured 78 of 78 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

78 of 78 outbound references displayed

  • verified exact2
  • verified fuzzy14
  • unresolved62
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a6aada62-a189-4e33-9549-1650ab9e2161 · outbound

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

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Language Models (Mostly) Know What They Know

Reference 1

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Observation e13dca3d-a1ae-43c1-91f1-6988888025e7 · outbound

This paper cites Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden States.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden States

Reference 2

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source=pdf_text observed=2026-08-07T11:23:37.172321Z digest=sha256:53f5fc7769562de042f7b2266d538c49d0b87d4856619be386c06a02a540b336

Observation 3dd6568f-91c2-4abf-8d10-fc541ea57efc · outbound

This paper cites Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation

Reference 3

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source=pdf_text observed=2026-08-07T11:23:37.308318Z digest=sha256:9be674c1567a063df17ec3cc4d4f3e233c9dbc4905dec90ed2e8f225a28df496

Observation d2f0973d-73fa-4a17-a726-0dc30061b0c9 · outbound

This paper cites SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Reference 4

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source=pdf_text observed=2026-08-07T11:23:37.525521Z digest=sha256:aa6a59fe8b61ac3fa74c781deace2e167918bf184c1ccda94b3cb5a2745b76e0

Observation a484e901-dd42-48fa-8405-eb64d2c50129 · outbound

This paper cites Language models are unsupervised multitask learners,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Language models are unsupervised multitask learners,

Reference 5

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source=pdf_text observed=2026-08-07T11:23:37.699908Z digest=sha256:900548690af9bda01afd5ebef48635f529dd657bfa1ad5b2dbb0a7f8fb6365ef

Observation 9f608241-1b84-4af8-9cac-5d993231fce7 · outbound

This paper cites Discovering Latent Knowledge in Language Models Without Supervision.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Discovering Latent Knowledge in Language Models Without Supervision

Reference 6

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source=pdf_text observed=2026-08-07T11:23:37.791029Z digest=sha256:1031d71383642ec668604d9418e4ed6260625bc9022eb7fbe275a683e7bb9cca

Observation d4f06ad0-ba7b-4c6e-afea-e7733339c8f7 · outbound

This paper cites On calibration of modern neural networks,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations On calibration of modern neural networks,

Reference 7

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source=pdf_text observed=2026-08-07T11:23:37.952312Z digest=sha256:ef77c1c0e047d3d48fbb94661e02029a723202da1a2d12219b4abc8f26878067

Observation f643f9c6-9c6d-4036-9cc5-12bd6ef203e0 · outbound

This paper cites How Reliable are LLMs as Knowledge Bases? Re-thinking Facutality and Consistency.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations How Reliable are LLMs as Knowledge Bases? Re-thinking Facutality and Consistency

Reference 8

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source=pdf_text observed=2026-08-07T11:23:38.140928Z digest=sha256:1fcce98bc4b0781738b761cb65cd7432fe24856585203d2b0777d9b7c7902d21

Observation 58680221-8b03-4db3-ab3f-313cdab70981 · outbound

This paper cites Haloscope: Harnessing unlabeled llm generations for hallucination detection,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Haloscope: Harnessing unlabeled llm generations for hallucination detection,

Reference 9

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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.

source=pdf_text observed=2026-08-07T11:23:38.310875Z digest=sha256:d230c3edea84751698a86a24c083d1622d6cff3c0ffede5e2a851160f173b745

Observation f967f485-fd6b-4da7-8739-61b39282550b · outbound

This paper cites INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection

Reference 10

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source=pdf_text observed=2026-08-07T11:23:38.462960Z digest=sha256:cf9bfb598ad89554c4a9acb430bd6f2269bb6d8f5f4c61f0f939e2dc7440ce40

Observation 94983dc2-615d-4168-9ff3-066b1f2b1229 · outbound

This paper cites Steer LLM Latents for Hallucination Detection.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Steer LLM Latents for Hallucination Detection

Reference 11

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source=pdf_text observed=2026-08-07T11:23:38.605975Z digest=sha256:34f7e8603b89ee4cb5cd8a27bfc514f1875d86609d971bc0ad7dc7e5e138d1c0

Observation 1eba682c-aa9e-463a-a87c-0161c93c0337 · outbound

This paper cites Survey of hallucination in natural language generation,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Survey of hallucination in natural language generation,

Reference 12

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source=pdf_text observed=2026-08-07T11:23:38.750103Z digest=sha256:7847a829c9f954c6acf538b359f32aac4b18ae69a307a8599cad27f40b0c4a20

Observation 5d11901c-b8a7-4c1b-981d-f425526a4057 · outbound

This paper cites HalluShift: Measuring Distribution Shifts towards Hallucination Detection in LLMs.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations HalluShift: Measuring Distribution Shifts towards Hallucination Detection in LLMs

Reference 13

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local_arxiv, observed 2026-08-07T11:23:48.912076Z

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

source=pdf_text observed=2026-08-07T11:23:38.916591Z digest=sha256:eba99c2961258036f63de00f6350078f6431003063812b8278736969be4cc1a1

Observation 59664ae5-ad43-4133-bc69-e0bac1c65dcd · outbound

This paper cites The Llama 3 Herd of Models.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations The Llama 3 Herd of Models

Reference 14

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Observation 68e68298-f44e-4de9-a872-c8cc95c622ea · outbound

This paper cites Qwen2.5 Technical Report.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Qwen2.5 Technical Report

Reference 15

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source=pdf_text observed=2026-08-07T11:23:39.262262Z digest=sha256:a2579836d6d13fa3ad3f404c38ce5341edc93f4224c092fb31b298a7b83214a7

Observation fd8423b2-e271-4dfa-9113-d68c794c0860 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 16

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Observation f6c06f6a-bf00-4fcc-b317-2e755258bfb9 · outbound

This paper cites Biases in large language models: origins, inventory, and discussion,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Biases in large language models: origins, inventory, and discussion,

Reference 17

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

source=pdf_text observed=2026-08-07T11:23:39.524806Z digest=sha256:d23a260379e7183b0e176747b83407b53aa110fee8a062c0c0175918599f3cda

Observation cbedf981-c3f4-4d84-8606-fc357a804858 · outbound

This paper cites Do large language models know how much they know?.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Do large language models know how much they know?

Reference 18

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

source=pdf_text observed=2026-08-07T11:23:39.662217Z digest=sha256:78288b09d1816cdd557f7a01f6c6a14e6f32c16908e865f64542534218a401d4

Observation 7ecf3284-8b16-4a89-a7d3-cca5ee1a24cc · outbound

This paper cites Self-evaluation improves selective generation in large language models,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Self-evaluation improves selective generation in large language models,

Reference 19

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

source=pdf_text observed=2026-08-07T11:23:39.873362Z digest=sha256:496bd72f445eeaec3e7cd91cb2906f61557ba776bd97f36d68b4d47c25851d8e

Observation 456412ab-7e65-42a1-bd13-e3f0578b0bca · outbound

This paper cites Deep Information Propagation.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Deep Information Propagation

Reference 20

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Observation 45024b38-fe8e-4cd8-a652-cb0dbcefcca1 · outbound

This paper cites Universal Adversarial Triggers for Attacking and Analyzing NLP.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Universal Adversarial Triggers for Attacking and Analyzing NLP

Reference 21

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Observation fc9e9816-7330-44af-afd3-e6dc70848119 · outbound

This paper cites Gradient-based Adversarial Attacks against Text Transformers.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Gradient-based Adversarial Attacks against Text Transformers

Reference 22

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Observation d02c014f-a360-4515-8af1-387e1ea08fb2 · outbound

This paper cites Identifying and Controlling Important Neurons in Neural Machine Translation.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Identifying and Controlling Important Neurons in Neural Machine Translation

Reference 23

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Observation bb8e5ba2-d6cc-4628-a473-88fb6b030bb0 · outbound

This paper cites A primer in bertology: What we know about how bert works,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations A primer in bertology: What we know about how bert works,

Reference 24

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

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Observation d23424c2-3b8b-4c04-a2ac-188e09215dd7 · outbound

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

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations How can we know when language models know? on the calibration of language models for question answering,

Reference 25

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Observation 23b8adce-b2fe-4285-b60f-32303e7e3a8f · outbound

This paper cites Reducing negative effects of the biases of language models in zero-shot setting,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Reducing negative effects of the biases of language models in zero-shot setting,

Reference 26

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

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Observation b20e8c74-299a-48a9-8e10-cdaf1cc310cb · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Understanding the difficulty of training deep feedforward neural networks,

Reference 27

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source=pdf_text observed=2026-08-07T11:23:40.759509Z digest=sha256:cf3bf71e0292900547ce278c579967412bb2743147c5bdd70ec9a130d77e51db

Observation b9817d44-5010-4322-bb1e-0f98bdb81c1f · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations A simple framework for contrastive learning of visual representations,

Reference 28

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Observation 91a5eb89-37a5-4b44-acc6-78a113a3ced7 · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations BERTScore: Evaluating Text Generation with BERT

Reference 29

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Observation 7c708084-f4df-4e44-836a-33b4c085273b · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Momentum contrast for unsupervised visual representation learning,

Reference 30

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source=pdf_text observed=2026-08-07T11:23:41.075071Z digest=sha256:e96c370c317d770ea11284128f4a46985b6bd4a381e45d56edb8017ea986659f

Observation dfe58951-8192-4325-9fc8-c113fd7e8d53 · outbound

This paper cites Euclidean distance mapping,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Euclidean distance mapping,

Reference 31

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

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Observation c9fad90e-4fa9-4782-ae4f-96fd697e93ff · outbound

This paper cites Analysis of euclidean distance and manhattan distance measure in face recognition,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Analysis of euclidean distance and manhattan distance measure in face recognition,

Reference 32

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raw_fallback, observed 2026-08-07T11:23:50.523585Z

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.

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Observation 04efed91-0e23-4278-a256-1be863806417 · outbound

This paper cites Coqa: A conversational question answering challenge,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Coqa: A conversational question answering challenge,

Reference 33

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raw_fallback, observed 2026-08-07T11:23:50.299438Z

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

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Observation bf92c682-d26e-478b-a330-abb02999c550 · outbound

This paper cites TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 34

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Observation a0d47af5-ac14-4089-9e05-78ac4331b319 · outbound

This paper cites Tydi qa: A benchmark for information-seeking question answering in ty pologically di verse languages,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Tydi qa: A benchmark for information-seeking question answering in ty pologically di verse languages,

Reference 35

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raw_fallback, observed 2026-08-07T11:23:50.106476Z

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.

source=pdf_text observed=2026-08-07T11:23:41.821063Z digest=sha256:9635923e15833fe008e23ca6261c32c8b915bcab08016ad296dd30d156556ad9

Observation 0fc7a139-0e5a-43f7-a1ab-60591432bb02 · outbound

This paper cites Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models

Reference 36

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source=pdf_text observed=2026-08-07T11:23:41.997814Z digest=sha256:6008fa8e9e62cd49f3ac27ef8b09883a091186cbb30ae9c429f431a2e5d85857

Observation fbda5082-a18b-4181-9837-72c64ac84e84 · outbound

This paper cites Out-of-Distribution Detection and Selective Generation for Conditional Language Models.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Out-of-Distribution Detection and Selective Generation for Conditional Language Models

Reference 37

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source=pdf_text observed=2026-08-07T11:23:42.160385Z digest=sha256:04c7f71379279fa1d4bb9dabfef2318d178ac80714e1d8783a6a293117938a13

Observation 22c2ac12-2086-4f65-829c-0af928c81182 · outbound

This paper cites Inference-time intervention: Eliciting truthful answers from a language model,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Inference-time intervention: Eliciting truthful answers from a language model,

Reference 38

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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.

source=pdf_text observed=2026-08-07T11:23:42.343495Z digest=sha256:7e10c295206a150b2e3c42d3d0599f49ee1b0a4a5404367de338ff4c55a5581a

Observation 24469ef4-053f-4a0a-9a67-9feaa557907b · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 39

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source=pdf_text observed=2026-08-07T11:23:42.499906Z digest=sha256:b7f4baad05994bd51034f05c1bc624ba9b1edb4ae1198a642d8202208a35fba6

Observation 4208402c-ff05-4224-9fab-719a9c7202f4 · outbound

This paper cites Uncertainty Estimation in Autoregressive Structured Prediction.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Uncertainty Estimation in Autoregressive Structured Prediction

Reference 40

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source=pdf_text observed=2026-08-07T11:23:42.671641Z digest=sha256:9daae703e1fd8d9e6c613b59fcab62d2a1fe3d9e69c21a970cbef5d70be502a6

Observation 483bec7f-7589-4b4b-8cab-bc2c9fe91469 · outbound

This paper cites Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Reference 41

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source=pdf_text observed=2026-08-07T11:23:42.800652Z digest=sha256:957a4ac83fa92e07391667d6cafb0431c72d1e67009ccaa250c6cdda75f172a5

Observation c963c415-db47-4405-a559-cbd2b355aebd · outbound

This paper cites Teaching Models to Express Their Uncertainty in Words.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Teaching Models to Express Their Uncertainty in Words

Reference 42

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source=pdf_text observed=2026-08-07T11:23:42.958624Z digest=sha256:6f6985279f78e681f43620171443cb1d0a1939e0e37ee026eba815de68590b10

Observation 4c2735aa-e2fe-4278-8322-8d9e183a0dbe · outbound

This paper cites The Internal State of an LLM Knows When It's Lying.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations The Internal State of an LLM Knows When It's Lying

Reference 43

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source=pdf_text observed=2026-08-07T11:23:43.092590Z digest=sha256:c638d6009b6c796121e85cc7a7dea6f3c95adf77d4cf164c6882fd2aa35960f6

Observation ebb698d5-18de-4501-8efd-23991df54621 · outbound

This paper cites AlignScore: Evaluating Factual Consistency with a Unified Alignment Function.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations AlignScore: Evaluating Factual Consistency with a Unified Alignment Function

Reference 44

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source=pdf_text observed=2026-08-07T11:23:43.221847Z digest=sha256:5f57a9aba0cece08ca5b1ca11959f2e5bf290b5c54d21cae630839d7a6864e05

Observation 21bcdcba-292d-4976-9ebb-f2783fe920e5 · outbound

This paper cites On early detection of hallucinations in factual question answering,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations On early detection of hallucinations in factual question answering,

Reference 45

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raw_fallback, observed 2026-08-07T11:23:49.821012Z

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.

source=pdf_text observed=2026-08-07T11:23:43.374068Z digest=sha256:1daae3ed5d301d76dba11c0ce3dd2ed059d70343c493dd667d97449762f96d45

Observation d31dde16-86d0-4681-a7f7-af4c48898683 · outbound

This paper cites Embedding and gradient say wrong: A white-box method for hallucination detection,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Embedding and gradient say wrong: A white-box method for hallucination detection,

Reference 46

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raw_fallback, observed 2026-08-07T11:23:49.678116Z

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.

source=pdf_text observed=2026-08-07T11:23:43.515069Z digest=sha256:8b946c76bf2a494b67c4856eeee43a29828292075fe0c0ea7c3fa754bd5b9715

Observation cc69d0a9-6165-46c7-a44f-931163779e6d · outbound

This paper cites BLEURT: Learning Robust Metrics for Text Generation.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations BLEURT: Learning Robust Metrics for Text Generation

Reference 47

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source=pdf_text observed=2026-08-07T11:23:43.738463Z digest=sha256:75060ed8f8b5155d76684f98e2b76a6a75569d34242e2dce9979a1cfd053938b

Observation 72752119-968d-445a-b31b-7c4807c0df0a · outbound

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

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Rouge: A package for automatic evaluation of summaries,

Reference 48

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source=pdf_text observed=2026-08-07T11:23:43.896627Z digest=sha256:1002b4273b4148234d85c0ad0ee2b2302523dc4e6eef70905fab65138462bcce

Observation 6ff1032a-dcdf-4b4c-80f4-7abc774b812e · outbound

This paper cites DeepSeek-V3 Technical Report.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations DeepSeek-V3 Technical Report

Reference 49

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source=pdf_text observed=2026-08-07T11:23:44.019560Z digest=sha256:d944ed7f0faa91061282320af1984ef356276f69a11f202ab092203a0b0e187a

Observation f5ae61ee-bd36-4ce2-9ef0-a344ad03c88d · outbound

This paper cites Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps

Reference 50

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source=pdf_text observed=2026-08-07T11:23:44.209036Z digest=sha256:769093577d7441d20fd3e1770582a133a925d12466cf5ba2f05b26f2d7c6dc8d

Observation f9288c02-e31b-447a-a72a-5e813e6491fe · outbound

This paper cites Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models

Reference 51

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source=pdf_text observed=2026-08-07T11:23:44.373261Z digest=sha256:858e8415912c3b66791ca8a70de338d3ad6f6ed065d82a8f0f4b7534dd33916c

Observation 3c13999c-22d8-42dc-8968-087a00fb4ab5 · outbound

This paper cites I-divergence geometry of probability distributions and minimization problems,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations I-divergence geometry of probability distributions and minimization problems,

Reference 52

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verified fuzzy
raw_fallback, observed 2026-08-07T11:23:49.568560Z

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.

source=pdf_text observed=2026-08-07T11:23:44.479476Z digest=sha256:94b5942be73d6ce52fda2adb7baecc21b3ce70c4870483e82185f5974480d57b

Observation 4904a3a4-b406-4734-bb9b-915e9a22d8d3 · outbound

This paper cites Attention is all you need,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Attention is all you need,

Reference 53

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source=pdf_text observed=2026-08-07T11:23:44.604999Z digest=sha256:8365e3f0bbedf3955426a27fa7eb8392e3deaead8550487628d5bbf0f79fcdf1

Observation 3e46d6e6-dc15-4835-9537-65b0a40ce11d · outbound

This paper cites GPT-4 Technical Report.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations GPT-4 Technical Report

Reference 54

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source=pdf_text observed=2026-08-07T11:23:44.768814Z digest=sha256:6056d2df3ccfc2ba94f740c8014dc4a253a80ccea582043c870a6757dba5571e

Observation c5bb57ae-eaa3-4475-8d62-b573b239cbbd · outbound

This paper cites A Survey on Hallucination in Large Vision-Language Models.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations A Survey on Hallucination in Large Vision-Language Models

Reference 55

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source=pdf_text observed=2026-08-07T11:23:44.873695Z digest=sha256:0227ca765e7abbb03587a88cda7e163b0316b44ab4dafd99deea2d9b5c58119f

Observation 4e0a679f-ee0f-46e3-8c92-8cd05a58a174 · outbound

This paper cites The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets

Reference 56

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source=pdf_text observed=2026-08-07T11:23:44.986161Z digest=sha256:65262520438fbeafef0f161704ac62ee4e9c8f5f8b0f80722ea67b4c51281c50

Observation 83fc8e36-c515-4ea8-8b13-6fd684cbbffb · outbound

This paper cites Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension

Reference 57

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source=pdf_text observed=2026-08-07T11:23:45.108804Z digest=sha256:6f3faced9239ec91597495fbf8d72828d1fa5302e54a9297df1858918d10fcca

Observation 5fe42bd1-73cf-404c-9d67-1919715eaac1 · outbound

This paper cites Visual prompt tuning,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Visual prompt tuning,

Reference 58

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source=pdf_text observed=2026-08-07T11:23:45.193691Z digest=sha256:029fb39450f1469d58d09f229f7a4bece74bd4799e8396276e54f857f2147112

Observation a3f20e0a-5b5b-48f4-8da6-79bff4b2f160 · outbound

This paper cites Locating and editing factual associations in gpt,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Locating and editing factual associations in gpt,

Reference 59

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source=pdf_text observed=2026-08-07T11:23:45.336650Z digest=sha256:c8ec5dc25b6f84d8f911e328038f17374d3cc8b0ae8c2fc7e0f26bdc3232d1f8

Observation 07a64a04-b6b7-4c8e-9d83-fadbb5dd53f0 · outbound

This paper cites Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing

Reference 60

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source=pdf_text observed=2026-08-07T11:23:45.468259Z digest=sha256:a8d2d9c8bc8249787cfd381e6b1aa28f340040fa90c237d8f0c8deae35fb4f48

Observation 35372292-09de-4517-a573-9be3e3838505 · outbound

This paper cites Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models

Reference 61

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source=pdf_text observed=2026-08-07T11:23:45.557329Z digest=sha256:1eca005d1e0d046dac065a67e600140fd41a78c19bcde1cdf59cfcc0d30d04ea

Observation 46285b36-c596-4a1f-bac7-0bbd1f43205f · outbound

This paper cites Attack and defense techniques in large language models: A survey and new perspectives.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Attack and defense techniques in large language models: A survey and new perspectives

Reference 62

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source=pdf_text observed=2026-08-07T11:23:45.644623Z digest=sha256:7824760af9024bd457eb8acc2540e7ca8a4af434a0269e5dc2c62587f12e7701

Observation 8e36142c-0ffc-4cdd-84cc-273cfcb4e4a5 · outbound

This paper cites Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation

Reference 63

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source=pdf_text observed=2026-08-07T11:23:45.748571Z digest=sha256:8ef39c0cf1790efb0e8a0fa1948cd041eec5c84727dfa10add04cc8bf0c68ab1

Observation 4e92d247-b511-49e5-8697-b48d91bde039 · outbound

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

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models

Reference 64

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source=pdf_text observed=2026-08-07T11:23:45.835103Z digest=sha256:7d9472c845490bd8ad012f5e9bd8a7e3e2d9025f87e34eb3490b61562c46500d

Observation a06ee7cb-4200-46b7-8ef2-8ba54f67a433 · outbound

This paper cites Alleviating Hallucinations of Large Language Models through Induced Hallucinations.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Alleviating Hallucinations of Large Language Models through Induced Hallucinations

Reference 65

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source=pdf_text observed=2026-08-07T11:23:45.951936Z digest=sha256:83709a90b61aa040113ec264edec04fd680a5d50d3fec8ffca55ff1cd1ff0e7e

Observation 5b4a3b55-afce-4dd1-b135-ea38af91e08b · outbound

This paper cites Hallucination is Inevitable: An Innate Limitation of Large Language Models.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Hallucination is Inevitable: An Innate Limitation of Large Language Models

Reference 66

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source=pdf_text observed=2026-08-07T11:23:46.035605Z digest=sha256:5b9d62f34b40215b0dfda3bd7aac7a17483cfbe05824464a1723f6d8fcb8c925

Observation 499cac6a-8f5e-49c6-8766-1c4e3ca6e5a4 · outbound

This paper cites Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus

Reference 67

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source=pdf_text observed=2026-08-07T11:23:46.152571Z digest=sha256:ea3c210188687944f1ec4244a080d222c49ecf9735f4eeb339b530e2444e1332

Observation f63e10c8-65af-448b-91b2-f936d0ba813d · outbound

This paper cites FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios

Reference 68

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:23:46.256762Z digest=sha256:bba64b58a4220b760841fbcb9b776d2135a6fa019dbe8d92194a56e07bea88d1

Observation b6e2147d-73ff-4580-8f69-bf70646ed4d0 · outbound

This paper cites FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Reference 69

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source=pdf_text observed=2026-08-07T11:23:46.330716Z digest=sha256:4395042464376bf53b049266c946379ab9639ff95a25f4aa573cf66997505fc0

Observation 9face940-a589-4fa0-9977-948c15f0fb97 · outbound

This paper cites Shifting attention to relevance: Towards the uncertainty estimation of large language models,.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Shifting attention to relevance: Towards the uncertainty estimation of large language models,

Reference 70

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raw_fallback, observed 2026-08-07T11:23:49.280808Z

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.

source=pdf_text observed=2026-08-07T11:23:46.384771Z digest=sha256:24c9445708f68fccc7df96fa38d6a0c9c3211d730b8059b7dbb0b1670b46193e

Observation b57bde36-9f10-4b05-afbc-5ab6c812289a · outbound

This paper cites Do Language Models Know When They're Hallucinating References?.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Do Language Models Know When They're Hallucinating References?

Reference 71

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:23:46.552464Z digest=sha256:74010ee78bfd30dc566bfac565b91b7f0446262bb4d12d4f42417f45f5b24862

Observation 0948e69e-ba72-4427-803b-3d784c73f858 · outbound

This paper cites LM vs LM: Detecting Factual Errors via Cross Examination.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations LM vs LM: Detecting Factual Errors via Cross Examination

Reference 72

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source=pdf_text observed=2026-08-07T11:23:46.698941Z digest=sha256:99948625186433bb23941b3f71f000c68bed02a0a3103da328f1521e97c9b710

Observation dd7a3d85-84cf-4bd9-9108-d6a711271719 · outbound

This paper cites Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation

Reference 73

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source=pdf_text observed=2026-08-07T11:23:46.895068Z digest=sha256:4a1fbe42c0dea042aef3585dd70976afacb645d85ad3c769b1d1feaff9080045

Observation f12a8b6b-719e-4ae1-96cc-9777a10f2fe7 · outbound

This paper cites Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback

Reference 74

Resolution
unresolved
no resolver link, observed 2026-08-07T11:23:47.056892Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:23:47.056892Z digest=sha256:b9d28624fe5bb2307cfd7749d2cca43305cf359509ab449e45805d02d9b358f0

Observation d29a3725-0fb8-44f3-8296-d5af0829891b · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-07T11:23:47.326925Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:23:47.326925Z digest=sha256:25e9c1fc8d362c70d79d41a5f80cf137ab920451b3284fed0b314305bbed8742

Observation c4835546-3ae7-4257-81a7-438e1fce90dc · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-07T11:23:47.399019Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:23:47.399019Z digest=sha256:51770a4e358e92f25beb3e6beac2bac5f4c1f763f07ca7b8b450a89cf84cd809

Observation cf273c16-e974-4c52-8d50-b417d4ebbcf9 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-07T11:23:47.471962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:23:47.471962Z digest=sha256:2da311d94815ec3e75e9f3c5cb6609e7d4ec198867e51b8011b885a22e8e4a32

Observation 2860fa1d-ef9a-4f8f-9252-3b355830d805 · outbound

This paper cites Sample-specific Masks for Visual Reprogramming-based Prompting.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Sample-specific Masks for Visual Reprogramming-based Prompting

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-07T11:23:47.566632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:23:47.566632Z digest=sha256:01f32fd0c1e22ac98136173d71aa76eae4697c2fd103157649408f64554f2220

Pith citing papers

No inbound Pith citation observations are available.