Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T20:05:49.514611Z
Paper Citation Record · LEDGER
As of 15 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 4 inbound Pith citation observations for arXiv:2411.10069.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T20:05:49.514611Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T05:42:46.597024Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T16:41:31.781711Z
45 of 45 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 4b0ab721-1103-4d2e-a036-04268c59ce97 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity GPT-4 Technical Report
Reference 1
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 2
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Observation 436de765-cffc-4bd4-afe1-46b3c1ecdb64 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 3
Source-reported events for the cited work
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Observation 0296d905-7868-47a9-8d5d-799bd7942fb4 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 4
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Observation 3081a4fe-8649-4ce2-b234-1d7f47eeb152 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Constitutional AI: Harmlessness from AI Feedback
Reference 5
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Observation 96c76790-c756-46eb-b1e1-c4b1c201e488 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Datasheet for the Pile
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Observation 2e096131-485c-4884-a06b-9aef46571326 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 7
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Observation 06d47a20-927f-40b6-958a-eae3a7235541 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 8
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Observation 53ea4357-45dc-4ab0-97f5-2baa772b87cb · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 9
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Observation 8cf1303c-bd96-47c4-bd51-7d71d6747662 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 10
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Observation 85ab5eb2-1d64-44e5-8db2-8328350baf2b · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 11
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Observation 5f076433-4623-4565-8527-df6af9f817d6 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Gonzalez, and et al
Reference 12
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Observation b234688c-8095-45d1-84af-e9d9de24dcae · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Glass, and Pengcheng He
Reference 13
Source-reported events for the cited work
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Observation 0c4c1877-01aa-4c15-9f84-1d0e603ce90b · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 14
Source-reported events for the cited work
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Observation f6f715fd-709a-4ea2-9536-a6905effb1d1 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 15
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Observation 04f69d7e-137d-4d3d-8e69-7a888d559e19 · outbound
Reference 16
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Observation a45d3b4f-b9ae-4400-9043-ea6e5aaf695e · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, and et al
Reference 17
Source-reported events for the cited work
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Observation a38736c9-414b-4d88-aef2-2d0fbee532c4 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
Reference 18
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Observation f5a56edd-fc22-4458-86d2-eee8ca407af5 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 19
Source-reported events for the cited work
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Observation a5368972-6635-41a5-9530-de4a7c7b8e6e · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Language Models (Mostly) Know What They Know
Reference 20
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Observation 29a952a5-da80-4967-9644-2eb3445330c2 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 21
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5149736e-628c-447b-ad8c-a20db645edf5 · outbound
Reference 22
Source-reported events for the cited work
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Observation 5b605cb2-0c80-4b15-8b59-2e15623dc0b6 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 23
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 24
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 25
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Observation 40d82a85-7a9d-4c7a-a637-a5868acbcd60 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 26
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Observation 923754be-a1ae-4525-99ec-84e2c1e5d37a · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 27
Source-reported events for the cited work
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 28
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Observation 93544346-e49b-4d69-8ed9-d7fc978ca4c6 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 29
Source-reported events for the cited work
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Observation 5d69361a-b3e3-44fe-b87b-49f1978e2b53 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Saarela and S
Reference 30
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Observation 408eede6-7f3e-4bba-9935-d703647660e0 · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Self-critiquing models for assisting human evaluators
Reference 31
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Observation 75e83ebe-f9a9-4bf3-bad2-e47d088707ea · outbound
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 32
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Manning, Andrew Ng, and Christopher Potts
Reference 33
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models
Reference 34
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 35
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 36
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 37
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity LLaMA: Open and Efficient Foundation Language Models
Reference 38
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 39
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Enhancing Large Language Models Against Inductive Instructions with Dual-critique Prompting
Reference 41
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 42
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity Unresolved cited work
Reference 43
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity online" 'onlinestring :=
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Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity write newline
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EM-MIAs: Enhancing Membership Inference Attacks in Large Language Models through Ensemble Modeling Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity
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Correctness Assessment of Code Generated by Large Language Models Using Internal Representations Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity
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Observation a606eed4-3d3f-455d-a0f3-2437f4da66c5 · inbound
Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity
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Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity
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