Paper Citation Record · LEDGER

Concept-Level Explainability for Auditing & Steering LLM Responses

As of 20 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 3 inbound Pith citation observations for arXiv:2505.07610.

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

pith.paper-citation-record.v1
2505.07610 v2

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:17:07.402209Z

measured 73 of 73 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:01:51.818562Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T09:05:36.555847Z

Reference resolution

70 of 70 outbound references displayed

verified exact2
verified fuzzy23
unresolved45
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bac0d01e-ce84-4d53-b949-dfc76ceef72b · outbound

This paper cites Hello gpt-4o.

Concept-Level Explainability for Auditing & Steering LLM Responses Hello gpt-4o

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.497141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 45fcea3c-3621-4852-b92e-68671c83e7eb · outbound

This paper cites The dark side of generative artificial intelligence: A critical analysis of controversies and risks of chatgpt.

Concept-Level Explainability for Auditing & Steering LLM Responses The dark side of generative artificial intelligence: A critical analysis of controversies and risks of chatgpt

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.483311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 86f892e6-3403-496a-b12a-3a745283dbc5 · outbound

This paper cites Survey of hallucination in natural language generation.

Concept-Level Explainability for Auditing & Steering LLM Responses Survey of hallucination in natural language generation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.104508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 318b843c-b369-4074-b837-e9db22e0b3a1 · outbound

This paper cites Jailbroken: How does llm safety training fail? Advances in Neural Information Processing Systems, 36:80079–80110, 2023.

Concept-Level Explainability for Auditing & Steering LLM Responses Jailbroken: How does llm safety training fail? Advances in Neural Information Processing Systems, 36:80079–80110, 2023

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.108993Z

Source-reported events for the cited work

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Observation 8eba732d-fa67-4ab9-a1b8-b3e9acf0c758 · outbound

This paper cites Spear Phishing With Large Language Models.

Concept-Level Explainability for Auditing & Steering LLM Responses Spear Phishing With Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.113423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b41396e7-6318-438e-9f90-ab13c566e92a · outbound

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

Concept-Level Explainability for Auditing & Steering LLM Responses Training language models to follow instructions with human feedback

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.118226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddf86fb3-7fa5-4abb-9ecb-0072a0c96075 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Concept-Level Explainability for Auditing & Steering LLM Responses Constitutional AI: Harmlessness from AI Feedback

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.123143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2b17460-a338-4259-9cac-7056b4ccb6a6 · outbound

This paper cites Pretraining language models with human preferences.

Concept-Level Explainability for Auditing & Steering LLM Responses Pretraining language models with human preferences

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.127814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dafeab6-0b5a-4734-b201-e5ca3d6c52c7 · outbound

This paper cites Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety.

Concept-Level Explainability for Auditing & Steering LLM Responses Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.131947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e11a20c-13d9-497c-a289-773f62e206fa · outbound

This paper cites Can LLM-Generated Misinformation Be Detected?.

Concept-Level Explainability for Auditing & Steering LLM Responses Can LLM-Generated Misinformation Be Detected?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.136572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3501c2d3-7799-4708-8c87-99a33c64db35 · outbound

This paper cites Ai model gpt-3 (dis) informs us better than humans.

Concept-Level Explainability for Auditing & Steering LLM Responses Ai model gpt-3 (dis) informs us better than humans

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.434478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 761c1b7a-e4c5-473d-b0ea-ac4b882f353b · outbound

This paper cites The operational risks of ai in large-scale biological attacks.

Concept-Level Explainability for Auditing & Steering LLM Responses The operational risks of ai in large-scale biological attacks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.420698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 064faa6d-e629-497f-b57b-b5bd301cce07 · outbound

This paper cites CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models.

Concept-Level Explainability for Auditing & Steering LLM Responses CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.150065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35081a60-27a7-4c0e-a018-57a1a893c672 · outbound

This paper cites LLM Agents can Autonomously Hack Websites.

Concept-Level Explainability for Auditing & Steering LLM Responses LLM Agents can Autonomously Hack Websites

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.154425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd4b3541-5924-427a-9a8c-f7905a998f12 · outbound

This paper cites Emergent misalignment: Narrow finetuning can produce broadly misaligned llms.

Concept-Level Explainability for Auditing & Steering LLM Responses Emergent misalignment: Narrow finetuning can produce broadly misaligned llms

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.159214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96908015-f375-4ffd-8af3-81ab4d62194d · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Concept-Level Explainability for Auditing & Steering LLM Responses Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.163342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b18ba5bb-3b5e-4ae5-bb6e-335684b6a272 · outbound

This paper cites Frontier Models are Capable of In-context Scheming.

Concept-Level Explainability for Auditing & Steering LLM Responses Frontier Models are Capable of In-context Scheming

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.168159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4b6a6ec-0b17-4d14-b23a-7911b5a49ecc · outbound

This paper cites Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era.

Concept-Level Explainability for Auditing & Steering LLM Responses Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.172594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa40994f-4b69-4860-a2c4-87a7da7808d0 · outbound

This paper cites TokenSHAP: Interpreting Large Language Models with Monte Carlo Shapley Value Estimation.

Concept-Level Explainability for Auditing & Steering LLM Responses TokenSHAP: Interpreting Large Language Models with Monte Carlo Shapley Value Estimation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.178029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa737317-87a5-41a2-9b0a-2edc2231b49b · outbound

This paper cites SyntaxShap: Syntax-aware Explainability Method for Text Generation.

Concept-Level Explainability for Auditing & Steering LLM Responses SyntaxShap: Syntax-aware Explainability Method for Text Generation

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:17:07.854631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f4d51d47-8053-46e0-a1dc-9d533c3f2ac1 · outbound

This paper cites Investigating the impact of linguistic errors of prompts on llm accuracy.

Concept-Level Explainability for Auditing & Steering LLM Responses Investigating the impact of linguistic errors of prompts on llm accuracy

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.406370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 908a955f-fc69-4801-ad0c-99db80f5b14c · outbound

This paper cites Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection.

Concept-Level Explainability for Auditing & Steering LLM Responses Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.191242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e0e68d1-fc97-44d9-a6f9-3a8cb601876f · outbound

This paper cites Conceptnet 5.5: An open multilingual graph of general knowledge.

Concept-Level Explainability for Auditing & Steering LLM Responses Conceptnet 5.5: An open multilingual graph of general knowledge

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.195619Z

Source-reported events for the cited work

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Observation e10233de-dd21-4122-b1f6-fb870fa5464a · outbound

This paper cites Hashimoto.

Concept-Level Explainability for Auditing & Steering LLM Responses Hashimoto

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.200529Z

Source-reported events for the cited work

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Observation c6397b1b-1d9b-471a-8ad0-ec57d9acdc38 · outbound

This paper cites Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM.

Concept-Level Explainability for Auditing & Steering LLM Responses Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.204518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8af50549-af41-4b6f-9a53-8f2a4e562276 · outbound

This paper cites Definitions, methods, and applications in interpretable machine learning.

Concept-Level Explainability for Auditing & Steering LLM Responses Definitions, methods, and applications in interpretable machine learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.208956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a628282d-7156-4ad7-b020-88ce0aa63fba · outbound

This paper cites Techniques for interpretable machine learning.

Concept-Level Explainability for Auditing & Steering LLM Responses Techniques for interpretable machine learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.364846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0a6ef210-56d7-4e90-840b-5a2182d8bbaa · outbound

This paper cites A survey of the state of explainable AI for natural language processing.

Concept-Level Explainability for Auditing & Steering LLM Responses A survey of the state of explainable AI for natural language processing

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.350187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a0cd394b-65d2-415c-ad01-510b20f8c705 · outbound

This paper cites On the explainability of natural language processing deep models.

Concept-Level Explainability for Auditing & Steering LLM Responses On the explainability of natural language processing deep models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.335689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 13c4ece8-690a-4a0f-ace5-89c65da0bf65 · outbound

This paper cites Interpretability in activation space analysis of transformers: A focused survey.

Concept-Level Explainability for Auditing & Steering LLM Responses Interpretability in activation space analysis of transformers: A focused survey

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.321415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 788db6e4-600c-4c9f-90b9-725ba6436a76 · outbound

This paper cites Neuron-level Interpretation of Deep NLP Models: A Survey.

Concept-Level Explainability for Auditing & Steering LLM Responses Neuron-level Interpretation of Deep NLP Models: A Survey

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.306137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0f800d80-f707-4fa9-9093-9e90f89f4ffb · outbound

This paper cites A value for n-person games.

Concept-Level Explainability for Auditing & Steering LLM Responses A value for n-person games

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.292011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 90792b78-b420-45d0-8099-b4a7e118abf5 · outbound

This paper cites why should i trust you?.

Concept-Level Explainability for Auditing & Steering LLM Responses why should i trust you?

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.278054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b6712469-0d7d-4b62-8a1c-c31bcfbb1190 · outbound

This paper cites BERT meets shapley: Extending SHAP explanations to transformer-based classifiers.

Concept-Level Explainability for Auditing & Steering LLM Responses BERT meets shapley: Extending SHAP explanations to transformer-based classifiers

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.264337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c0e73b65-4983-4c97-b7d1-3db6797eef80 · outbound

This paper cites Generating hierarchical explanations on text classification via feature interaction detection.

Concept-Level Explainability for Auditing & Steering LLM Responses Generating hierarchical explanations on text classification via feature interaction detection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.249633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a1f66367-8446-4169-bada-fe4da118d417 · outbound

This paper cites Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training.

Concept-Level Explainability for Auditing & Steering LLM Responses Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Reference 36

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Source-reported events for the cited work

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Observation ee143436-0090-4d70-b195-7e983c05ef79 · outbound

This paper cites Explainability for large language models: A survey.

Concept-Level Explainability for Auditing & Steering LLM Responses Explainability for large language models: A survey

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d37153b2-4a86-4c66-a0bc-3534713d77c6 · outbound

This paper cites Ten levels of ai alignment difficulty.

Concept-Level Explainability for Auditing & Steering LLM Responses Ten levels of ai alignment difficulty

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.227087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 409b987b-19a3-42f5-a77a-1d7e2f3d0b64 · outbound

This paper cites A Survey on Fairness in Large Language Models.

Concept-Level Explainability for Auditing & Steering LLM Responses A Survey on Fairness in Large Language Models

Reference 39

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Source-reported events for the cited work

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Observation b16f809f-dfdd-4179-a469-a33503eccbb8 · outbound

This paper cites Aligned probing: Relating toxic behavior and model internals.

Concept-Level Explainability for Auditing & Steering LLM Responses Aligned probing: Relating toxic behavior and model internals

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.269168Z

Source-reported events for the cited work

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Observation 61508227-3c81-450d-a549-ea966a2f268a · outbound

This paper cites Toy Models of Superposition.

Concept-Level Explainability for Auditing & Steering LLM Responses Toy Models of Superposition

Reference 41

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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This paper cites Formalizing convergent instrumental goals.

Concept-Level Explainability for Auditing & Steering LLM Responses Formalizing convergent instrumental goals

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.213540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 854b13ba-f2d2-49d3-90a5-3120df4fe382 · outbound

This paper cites Circumventing interpretability: How to defeat mind-readers.

Concept-Level Explainability for Auditing & Steering LLM Responses Circumventing interpretability: How to defeat mind-readers

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.282747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08c856c7-6096-4266-afe9-271843c6fa6b · outbound

This paper cites SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks.

Concept-Level Explainability for Auditing & Steering LLM Responses SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.287109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68ef9ad3-5011-4a51-81b3-be73ea613aee · outbound

This paper cites Parafuzz: An interpretability-driven technique for detecting poisoned samples in nlp.

Concept-Level Explainability for Auditing & Steering LLM Responses Parafuzz: An interpretability-driven technique for detecting poisoned samples in nlp

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.200116Z

Source-reported events for the cited work

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Observation 50b6c9e1-5818-47ac-ab9c-55025625e684 · outbound

This paper cites SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner.

Concept-Level Explainability for Auditing & Steering LLM Responses SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.295771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dea027d8-1962-4ffb-91e0-58686bb29653 · outbound

This paper cites Protecting your llms with information bottleneck.

Concept-Level Explainability for Auditing & Steering LLM Responses Protecting your llms with information bottleneck

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.186282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f4d68c51-1582-43be-ae90-20658e656865 · outbound

This paper cites Defending LLMs against Jailbreaking Attacks via Backtranslation.

Concept-Level Explainability for Auditing & Steering LLM Responses Defending LLMs against Jailbreaking Attacks via Backtranslation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.304943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7369042-7366-4822-93c2-1b76075e8ef7 · outbound

This paper cites IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks.

Concept-Level Explainability for Auditing & Steering LLM Responses IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:17:07.573180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f9ab14bf-c74d-4e11-b9f9-c94e9643bec2 · outbound

This paper cites Defending against Insertion-based Textual Backdoor Attacks via Attribution.

Concept-Level Explainability for Auditing & Steering LLM Responses Defending against Insertion-based Textual Backdoor Attacks via Attribution

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.314042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2c2490a-07fd-459f-8340-96c717217984 · outbound

This paper cites LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked.

Concept-Level Explainability for Auditing & Steering LLM Responses LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.318596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e27ea07c-a248-4f61-b078-a5a85af29c3e · outbound

This paper cites Multilingual Jailbreak Challenges in Large Language Models.

Concept-Level Explainability for Auditing & Steering LLM Responses Multilingual Jailbreak Challenges in Large Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.323068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78efed91-2622-40ac-8f34-ebea2440af36 · outbound

This paper cites Defending chatgpt against jailbreak attack via self-reminders.

Concept-Level Explainability for Auditing & Steering LLM Responses Defending chatgpt against jailbreak attack via self-reminders

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.327313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82cf7bc5-488a-405d-b066-6d3dacc68af0 · outbound

This paper cites Scaling and evaluating sparse autoencoders.

Concept-Level Explainability for Auditing & Steering LLM Responses Scaling and evaluating sparse autoencoders

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.331441Z

Source-reported events for the cited work

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

Concept-Level Explainability for Auditing & Steering LLM Responses Inference- time intervention: Eliciting truthful answers from a language model

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.335878Z

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Concept-Level Explainability for Auditing & Steering LLM Responses Towards monosemanticity: Decomposing language models with dictionary learning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.339995Z

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Concept-Level Explainability for Auditing & Steering LLM Responses Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.344144Z

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Observation 650c80e1-3e86-4142-b986-ce49f4974040 · outbound

This paper cites spacy: Industrial- strength natural language processing in python.

Concept-Level Explainability for Auditing & Steering LLM Responses spacy: Industrial- strength natural language processing in python

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.146288Z

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Observation 3e971645-5a0c-496d-ab41-69d6d18c3a65 · outbound

This paper cites Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers.

Concept-Level Explainability for Auditing & Steering LLM Responses Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.352934Z

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Concept-Level Explainability for Auditing & Steering LLM Responses Unresolved cited work

Reference 60

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Concept-Level Explainability for Auditing & Steering LLM Responses Unresolved cited work

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.362031Z

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This paper cites Gpt-4o mini: advancing cost-efficient intelligence, 2024.

Concept-Level Explainability for Auditing & Steering LLM Responses Gpt-4o mini: advancing cost-efficient intelligence, 2024

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.103285Z

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This paper cites Manning, Andrew Ng, and Christopher Potts.

Concept-Level Explainability for Auditing & Steering LLM Responses Manning, Andrew Ng, and Christopher Potts

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.089392Z

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Observation e9c8d542-69a1-4c08-bcc2-37d198805965 · outbound

This paper cites Analyzing sentiment polarity reduction in news presentation through contextual perturbation and large language models.

Concept-Level Explainability for Auditing & Steering LLM Responses Analyzing sentiment polarity reduction in news presentation through contextual perturbation and large language models

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.075044Z

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Observation b9f7cb44-8f69-4289-a243-29596037956f · outbound

This paper cites Interpreting and Steering LLMs with Mutual Information-based Explanations on Sparse Autoencoders.

Concept-Level Explainability for Auditing & Steering LLM Responses Interpreting and Steering LLMs with Mutual Information-based Explanations on Sparse Autoencoders

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.379461Z

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Observation e76e2382-4f89-44d7-bde0-4974df77e60f · outbound

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Concept-Level Explainability for Auditing & Steering LLM Responses SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.384006Z

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Observation 02a15849-097a-4bd7-9424-8167c6e7fb6e · outbound

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Concept-Level Explainability for Auditing & Steering LLM Responses Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:17:08.060462Z

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Observation 467cd83a-bdb6-480a-97bd-f5e86ec6082c · outbound

This paper cites Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks.

Concept-Level Explainability for Auditing & Steering LLM Responses Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.393168Z

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This paper cites Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.

Concept-Level Explainability for Auditing & Steering LLM Responses Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:08.046077Z

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Observation 81a50a37-21e6-4d3d-bd7d-35e67e9fedbc · outbound

This paper cites The Llama 3 Herd of Models.

Concept-Level Explainability for Auditing & Steering LLM Responses The Llama 3 Herd of Models

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:07.402209Z

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Pith citing papers

Observation fb72a985-5ae2-4caf-bdc6-5473b80e50bf · inbound

Can Global XAI Methods Reveal Injected Behaviours in LLMs? SHAP vs Rule Extraction vs RuleSHAP cites this paper.

Can Global XAI Methods Reveal Injected Behaviours in LLMs? SHAP vs Rule Extraction vs RuleSHAP Concept-Level Explainability for Auditing & Steering LLM Responses

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T21:01:51.818562Z

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Observation 9769ed81-c868-4115-a94f-e940e0998d1b · inbound

MPF: Aligning and Debiasing Language Models post Deployment via Multi Perspective Fusion cites this paper.

MPF: Aligning and Debiasing Language Models post Deployment via Multi Perspective Fusion Concept-Level Explainability for Auditing & Steering LLM Responses

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T20:33:25.666132Z

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Observation 8e9168fb-7653-4a6e-847c-0fd7ce1785c6 · inbound

Investigating Linguistic Steering: An Analysis of Adjectival Effects Across Large Language Model Architectures cites this paper.

Investigating Linguistic Steering: An Analysis of Adjectival Effects Across Large Language Model Architectures Concept-Level Explainability for Auditing & Steering LLM Responses

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-01T09:05:36.558082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.