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Paper Citation Record · LEDGER

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection

As of 8 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2506.01104.

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

pith.paper-citation-record.v1
2506.01104 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:55:47.454336Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

28 of 28 outbound references displayed

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  • verified fuzzy5
  • unresolved22
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External citation measurements

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Outbound references

Observation abee4586-2a68-44a7-9ff6-33e6f9fd4601 · outbound

This paper cites Weak to strong generalization for large language models with multi-capabilities,.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Weak to strong generalization for large language models with multi-capabilities,

Reference 1

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Observation 65d8ce03-d831-4464-9188-02da469f9e49 · outbound

This paper cites Fine-grained distillation for long document retrieval,.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Fine-grained distillation for long document retrieval,

Reference 2

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Observation 9a377266-8c81-4527-b85e-2724872511f3 · outbound

This paper cites Can users detect biases or factual errors in generated responses in conversational information-seeking?.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Can users detect biases or factual errors in generated responses in conversational information-seeking?

Reference 4

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Observation 203d514f-dbb4-47a0-ae70-9671350dd182 · outbound

This paper cites Challenges in information-seeking QA: unanswerable questions and paragraph retrieval,.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Challenges in information-seeking QA: unanswerable questions and paragraph retrieval,

Reference 5

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Observation 2e21b712-7185-44cb-82e0-25c0a4f16deb · outbound

This paper cites Attentional transfer is all you need: Technology-aware layout pattern generation,.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Attentional transfer is all you need: Technology-aware layout pattern generation,

Reference 6

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Observation 685ce54e-ec10-44ff-b3f6-36ad507d0513 · outbound

This paper cites Enhancing intent understanding for ambiguous prompts through human-machine co-adaptation,.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Enhancing intent understanding for ambiguous prompts through human-machine co-adaptation,

Reference 7

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Observation ae7a2baa-1a06-4811-8c18-64d710845ef0 · outbound

This paper cites Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion

Reference 8

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Observation 6e55c81c-63ab-4951-ba5f-fe1a4f77ea40 · outbound

This paper cites BERT: pre-training of deep bidirectional transformers for language understanding,.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection BERT: pre-training of deep bidirectional transformers for language understanding,

Reference 9

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Observation a5bf1de2-e972-4758-b4ca-4e5ea2f56f1b · outbound

This paper cites Claret: Pre-training a correlation-aware context-to-event transformer for event-centric gener- ation and classification,.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Claret: Pre-training a correlation-aware context-to-event transformer for event-centric gener- ation and classification,

Reference 10

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Observation 49e4cc2e-d563-4642-a2ff-d1f87b7ac98d · outbound

This paper cites Language models with image descriptors are strong few- shot video-language learners,.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Language models with image descriptors are strong few- shot video-language learners,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 11f4af90-e49b-440b-91ce-dc6031c31e4d · outbound

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

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 12

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation a48878c1-53bb-459b-82b4-8baf00154216 · outbound

This paper cites Less is more: 9 Vision representation compression for efficient video generation with large language models,.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Less is more: 9 Vision representation compression for efficient video generation with large language models,

Reference 13

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c104ff89-8b40-4863-b0de-1fe0088d9c1b · outbound

This paper cites Multimodal event transformer for image-guided story ending generation,.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Multimodal event transformer for image-guided story ending generation,

Reference 14

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Observation 596acd8c-1fdb-49ad-8231-7fde8c58df72 · outbound

This paper cites Scaling Laws for Neural Language Models.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Scaling Laws for Neural Language Models

Reference 15

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Observation 721a3d6e-8035-4b13-b078-d89beafc1812 · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection LaMDA: Language Models for Dialog Applications

Reference 16

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Observation d5cb9968-0c67-4872-97d8-cba35605460b · outbound

This paper cites Improving Medical Large Vision-Language Models with Abnormal-Aware Feedback.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Improving Medical Large Vision-Language Models with Abnormal-Aware Feedback

Reference 17

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Observation c7dafa88-ff5a-48ee-9003-137ae2bcd977 · outbound

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

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 18

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Observation 8a9d3577-c006-4afb-b19d-8be68bece20d · outbound

This paper cites Towards reliable and factual response generation: Detecting unanswerable questions in information-seeking conversations,.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Towards reliable and factual response generation: Detecting unanswerable questions in information-seeking conversations,

Reference 19

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Observation 71bf8bab-2498-4174-9156-a9a6985a0b29 · outbound

This paper cites FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization

Reference 20

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Observation 011a7abb-0ef2-403f-9cde-9794f10e06bb · outbound

This paper cites Available: https://doi.org/10.1007/978-3-031-56063-7 25.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Available: https://doi.org/10.1007/978-3-031-56063-7 25

Reference 21

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Observation 82828929-8fab-4373-be05-bb8f6006ad1d · outbound

This paper cites TRUE: Re-evaluating Factual Consistency Evaluation.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection TRUE: Re-evaluating Factual Consistency Evaluation

Reference 22

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Observation 2bed12ab-930d-4e24-8739-21209f61af62 · outbound

This paper cites Evaluating the Factual Consistency of Abstractive Text Summarization.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Evaluating the Factual Consistency of Abstractive Text Summarization

Reference 23

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Observation 7d9a0330-bfaa-4d50-928b-305acd7de436 · outbound

This paper cites Improving zero-shot cross-lingual transfer for multilingual question answering over knowledge graph,.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Improving zero-shot cross-lingual transfer for multilingual question answering over knowledge graph,

Reference 24

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Observation c93e7de5-6f82-4641-a0fe-09ddce9504d0 · outbound

This paper cites Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment

Reference 25

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Observation 2e8ff7b7-bf18-4627-9ba3-59d9b07c5670 · outbound

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

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 26

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Observation 909dee45-a45d-4231-bdd1-40b4e5269186 · outbound

This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 27

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Observation 24930c6b-27a9-4e59-b37e-142d3775b242 · outbound

This paper cites InstructPatentGPT: Training patent language models to follow instructions with human feedback.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection InstructPatentGPT: Training patent language models to follow instructions with human feedback

Reference 28

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Observation 0516d09d-4abb-4115-b569-b3df5e26dee7 · outbound

This paper cites A comprehensive method for model credibility measurement,.

Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection A comprehensive method for model credibility measurement,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Pith citing papers

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