Paper Citation Record · LEDGER

Fine-Grained Interpretation of Political Opinions in Large Language Models

As of 8 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2506.04774.

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

pith.paper-citation-record.v1
2506.04774 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:40:17.766202Z

measured 56 of 56 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T06:04:46.132037Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

55 of 55 outbound references displayed

verified exact2
verified fuzzy3
unresolved49
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 94ad6ea2-554f-42e1-ab19-04acb1203e09 · outbound

This paper cites an unresolved cited work.

Fine-Grained Interpretation of Political Opinions in Large Language Models Unresolved cited work

Reference 1

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Observation c9e68394-6eef-4cf3-a4b0-e7d8d5b7f83a · outbound

This paper cites Understanding Jailbreak Success: A Study of Latent Space Dynamics in Large Language Models.

Fine-Grained Interpretation of Political Opinions in Large Language Models Understanding Jailbreak Success: A Study of Latent Space Dynamics in Large Language Models

Reference 2

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Observation 47f9b4a8-e997-4fd1-8530-56578e50b46a · outbound

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Fine-Grained Interpretation of Political Opinions in Large Language Models Unresolved cited work

Reference 3

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Observation da2c91fc-be27-4121-968e-9506f3213a2d · outbound

This paper cites M.; Gebru, T.; McMillan-Major, A.; and Shmitchell, S.

Fine-Grained Interpretation of Political Opinions in Large Language Models M.; Gebru, T.; McMillan-Major, A.; and Shmitchell, S

Reference 4

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Observation 72f9e6b0-5781-4e0d-a4de-4a2be5e897e7 · outbound

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Fine-Grained Interpretation of Political Opinions in Large Language Models Towards Unifying Interpretability and Control: Evaluation via Intervention

Reference 5

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Observation 77a8d546-c9e6-436e-a5c6-3503ba20ae16 · outbound

This paper cites Counterfactual Token Generation in Large Language Models.

Fine-Grained Interpretation of Political Opinions in Large Language Models Counterfactual Token Generation in Large Language Models

Reference 6

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Observation 1882d3a5-a3cb-48dd-8294-f3b59f8a8415 · outbound

This paper cites Designing a Dashboard for Transparency and Control of Conversational AI.

Fine-Grained Interpretation of Political Opinions in Large Language Models Designing a Dashboard for Transparency and Control of Conversational AI

Reference 7

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Fine-Grained Interpretation of Political Opinions in Large Language Models Unresolved cited work

Reference 8

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Observation 77282ff0-8995-45d1-bd74-e13880b584dc · outbound

This paper cites Plug and Play Language Models: A Simple Approach to Controlled Text Generation.

Fine-Grained Interpretation of Political Opinions in Large Language Models Plug and Play Language Models: A Simple Approach to Controlled Text Generation

Reference 9

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Fine-Grained Interpretation of Political Opinions in Large Language Models Unresolved cited work

Reference 10

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Observation f2e26a96-310f-4b30-9321-10844ac4f91c · outbound

This paper cites Toy Models of Superposition.

Fine-Grained Interpretation of Political Opinions in Large Language Models Toy Models of Superposition

Reference 11

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Observation e3f6b74e-ae22-427d-9c24-c40fa118f0a0 · outbound

This paper cites Detecting Strategic Deception Using Linear Probes.

Fine-Grained Interpretation of Political Opinions in Large Language Models Detecting Strategic Deception Using Linear Probes

Reference 12

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Observation 64cc8a35-8753-4c2f-b366-3a6dc1e96ae1 · outbound

This paper cites The Llama 3 Herd of Models.

Fine-Grained Interpretation of Political Opinions in Large Language Models The Llama 3 Herd of Models

Reference 13

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This paper cites Language Models Represent Space and Time.

Fine-Grained Interpretation of Political Opinions in Large Language Models Language Models Represent Space and Time

Reference 14

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Fine-Grained Interpretation of Political Opinions in Large Language Models Unresolved cited work

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Fine-Grained Interpretation of Political Opinions in Large Language Models Unresolved cited work

Reference 17

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Observation 119a767e-c9ed-4308-9bd7-8ba1b88330db · outbound

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Fine-Grained Interpretation of Political Opinions in Large Language Models Unresolved cited work

Reference 18

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This paper cites LLM Internal States Reveal Hallucination Risk Faced With a Query.

Fine-Grained Interpretation of Political Opinions in Large Language Models LLM Internal States Reveal Hallucination Risk Faced With a Query

Reference 19

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Observation 141e02df-241b-4a32-9d7b-cc0abad9b370 · outbound

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Fine-Grained Interpretation of Political Opinions in Large Language Models Mistral 7B

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Fine-Grained Interpretation of Political Opinions in Large Language Models Unresolved cited work

Reference 21

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This paper cites Benchmarking Multi-National Value Alignment for Large Language Models.

Fine-Grained Interpretation of Political Opinions in Large Language Models Benchmarking Multi-National Value Alignment for Large Language Models

Reference 22

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Fine-Grained Interpretation of Political Opinions in Large Language Models Are Sparse Autoencoders Useful? A Case Study in Sparse Probing

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Fine-Grained Interpretation of Political Opinions in Large Language Models Linear Representations of Political Perspective Emerge in Large Language Models

Reference 24

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Observation 16f20382-c307-4930-873a-f4d579f23c8a · outbound

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Fine-Grained Interpretation of Political Opinions in Large Language Models The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets

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Fine-Grained Interpretation of Political Opinions in Large Language Models Auditing language models for hidden objectives

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Observation b4ff2cd8-e959-4103-aaa2-566c0541250a · outbound

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Fine-Grained Interpretation of Political Opinions in Large Language Models Unresolved cited work

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Fine-Grained Interpretation of Political Opinions in Large Language Models Unresolved cited work

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Fine-Grained Interpretation of Political Opinions in Large Language Models Steering Llama 2 via Contrastive Activation Addition

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Fine-Grained Interpretation of Political Opinions in Large Language Models The Linear Representation Hypothesis and the Geometry of Large Language Models

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Fine-Grained Interpretation of Political Opinions in Large Language Models Probing the Subtle Ideological Manipulation of Large Language Models

Reference 33

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Fine-Grained Interpretation of Political Opinions in Large Language Models Emergence of human-like polarization among large language model agents

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Fine-Grained Interpretation of Political Opinions in Large Language Models Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters

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Fine-Grained Interpretation of Political Opinions in Large Language Models Towards Reliable Evaluation of Behavior Steering Interventions in LLMs

Reference 36

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Observation a76be71c-2681-4501-b4f8-ee2b4f620f18 · outbound

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Fine-Grained Interpretation of Political Opinions in Large Language Models Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models

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Observation 7696cd5b-55a8-4088-b0f2-1a8750d958c1 · outbound

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Fine-Grained Interpretation of Political Opinions in Large Language Models Extracting Latent Steering Vectors from Pretrained Language Models

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Fine-Grained Interpretation of Political Opinions in Large Language Models Mechanistic Interpretability of Emotion Inference in Large Language Models

Reference 40

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Fine-Grained Interpretation of Political Opinions in Large Language Models Unmasking Implicit Bias: Evaluating Persona-Prompted LLM Responses in Power-Disparate Social Scenarios

Reference 41

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Observation a02a8b38-77f9-4cce-906d-774e013f5b71 · outbound

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Fine-Grained Interpretation of Political Opinions in Large Language Models Gemma 3 Technical Report

Reference 42

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Fine-Grained Interpretation of Political Opinions in Large Language Models M.; Thiergart, L.; Leech, G.; Udell, D.; Vazquez, J

Reference 43

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Observation 42f5e57b-87e7-4b21-8626-8562d130f76d · outbound

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Fine-Grained Interpretation of Political Opinions in Large Language Models Steering Language Models With Activation Engineering

Reference 44

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Fine-Grained Interpretation of Political Opinions in Large Language Models Unresolved cited work

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Fine-Grained Interpretation of Political Opinions in Large Language Models D.; Cislak, A.; and Schmidt, P

Reference 47

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Fine-Grained Interpretation of Political Opinions in Large Language Models AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders

Reference 48

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Fine-Grained Interpretation of Political Opinions in Large Language Models pyvene: A Library for Understanding and Improving PyTorch Models via Interventions

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Reference 50

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Observation a9827d7b-aa56-4b04-8caa-010647adb60f · outbound

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Fine-Grained Interpretation of Political Opinions in Large Language Models Qwen3 Technical Report

Reference 51

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Fine-Grained Interpretation of Political Opinions in Large Language Models Beyond Single Concept Vector: Modeling Concept Subspace in LLMs with Gaussian Distribution

Reference 52

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Fine-Grained Interpretation of Political Opinions in Large Language Models Representation Engineering: A Top-Down Approach to AI Transparency

Reference 53

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Fine-Grained Interpretation of Political Opinions in Large Language Models , " * write output.state after.block = add.period write newline

Reference 54

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Fine-Grained Interpretation of Political Opinions in Large Language Models write newline

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Pith citing papers

Observation 8fde43a3-2aed-4689-b425-0635613a9c73 · inbound

Agent Security Needs Redefinition through a Holistic Framework cites this paper.

Agent Security Needs Redefinition through a Holistic Framework Fine-Grained Interpretation of Political Opinions in Large Language Models

Reference 169

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