Breaking Down Bias: On The Limits of Generalizable Pruning Strategies | Paper Citation Record

Observation 9e3def6a-9c0b-467c-8c35-00efcfef14ec · outbound

This paper cites Attention Speaks Volumes: Localizing and Mitigating Bias in Language Models.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Attention Speaks Volumes: Localizing and Mitigating Bias in Language Models

Reference 1

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This paper cites Mean Difference, Standardized Mean Difference (SMD), and Their Use in Meta-Analysis: As Simple as It Gets.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Mean Difference, Standardized Mean Difference (SMD), and Their Use in Meta-Analysis: As Simple as It Gets

Reference 2

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Observation 4bdf0417-68f3-4a87-8fe5-e8422cd505b7 · outbound

This paper cites Measuring Implicit Bias in Explicitly Unbiased Large Language Models.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Measuring Implicit Bias in Explicitly Unbiased Large Language Models

Reference 3

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This paper cites Taking the next step with generative artificial intelligence: The transformative role of multimodal large language models in science education.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Taking the next step with generative artificial intelligence: The transformative role of multimodal large language models in science education

Reference 4

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This paper cites On the Opportunities and Risks of Foundation Models.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies On the Opportunities and Risks of Foundation Models

Reference 5

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This paper cites Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions

Reference 6

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This paper cites General purpose technologies.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies General purpose technologies

Reference 7

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This paper cites Prompting change: exploring prompt engineering in large language model AI and its potential to transform education.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Prompting change: exploring prompt engineering in large language model AI and its potential to transform education

Reference 8

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Observation a4343104-c8a9-4418-9765-cea366588603 · outbound

This paper cites Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks

Reference 9

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Observation db760dd2-a826-4346-a9d6-e1b0cda7ccae · outbound

This paper cites Memorized Images in Diffusion Models share a Subspace that can be Located and Deleted.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Memorized Images in Diffusion Models share a Subspace that can be Located and Deleted

Reference 10

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This paper cites An overview of domain-specific foundation model: key technolo- gies, applications and challenges.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies An overview of domain-specific foundation model: key technolo- gies, applications and challenges

Reference 11

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This paper cites Crime News and Racialized Beliefs: Understanding the Relationship Between Local News Viewing and Perceptions of African Americans and Crime.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Crime News and Racialized Beliefs: Understanding the Relationship Between Local News Viewing and Perceptions of African Americans and Crime

Reference 12

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This paper cites Evaluating Feature Steering: A Case Study in Mitigating Social Bi- ases.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Evaluating Feature Steering: A Case Study in Mitigating Social Bi- ases

Reference 13

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Observation d50a8fa8-77f6-4b5a-bcc3-c4aeffde918c · outbound

This paper cites First-Person Fairness in Chatbots.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies First-Person Fairness in Chatbots

Reference 14

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This paper cites The democratization of global AI governance and the role of tech companies.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies The democratization of global AI governance and the role of tech companies

Reference 15

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This paper cites Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial In- telligence Act).

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial In- telligence Act)

Reference 16

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Observation f876fbe9-a48f-418d-bdad-062dcdb50975 · outbound

This paper cites Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies

Reference 17

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This paper cites How black are Lakisha and Jamal? Racial perceptions from names used in correspondence audit studies.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies How black are Lakisha and Jamal? Racial perceptions from names used in correspondence audit studies

Reference 18

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Observation 751caf8e-eca2-4170-a6ff-ec9796f26cff · outbound

This paper cites The Capacity for Moral Self-Correction in Large Language Models.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies The Capacity for Moral Self-Correction in Large Language Models

Reference 19

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Observation c63d21f2-b9fd-44d2-a3cc-c5dd96295939 · outbound

This paper cites From Melting Pots to Misrepresentations: Exploring Harms in Generative AI.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies From Melting Pots to Misrepresentations: Exploring Harms in Generative AI

Reference 20

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This paper cites Prime Suspects: The Influence of Local Television News on the Viewing Public.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Prime Suspects: The Influence of Local Television News on the Viewing Public

Reference 21

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This paper cites Where You Live and What You Watch: The Impact of Racial Proximity and Local Television News on Attitudes about Race and Crime.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Where You Live and What You Watch: The Impact of Racial Proximity and Local Television News on Attitudes about Race and Crime

Reference 22

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Observation 97349f26-dd4c-4d37-9cc6-53a5e12456bf · outbound

This paper cites Police agencies on Facebook overreport on Black suspects.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Police agencies on Facebook overreport on Black suspects

Reference 23

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Observation c89f36a0-a5c4-45ab-8e6b-b2fa98c33e56 · outbound

This paper cites Generative Discrimination: What Happens When Generative AI Exhibits Bias, and What Can Be Done About It.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Generative Discrimination: What Happens When Generative AI Exhibits Bias, and What Can Be Done About It

Reference 24

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This paper cites What's in a Name? Auditing Large Language Models for Race and Gender Bias.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies What's in a Name? Auditing Large Language Models for Race and Gender Bias

Reference 25

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This paper cites Ethical AI: A Policy Framework to Regulate Bias in Large Language Models.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Ethical AI: A Policy Framework to Regulate Bias in Large Language Models

Reference 26

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Observation 37376df4-4fb5-4459-9aa7-b69105a40f7d · outbound

This paper cites Financial Statement Analysis with Large Language Models.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Financial Statement Analysis with Large Language Models

Reference 27

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Observation a90acf44-587e-4f5c-b3e1-0f3f798ec1c8 · outbound

This paper cites “We’d love to hire them, but.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies “We’d love to hire them, but

Reference 28

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This paper cites Acceptable Use Policies for Foundation Models.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Acceptable Use Policies for Foundation Models

Reference 29

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This paper cites Gender bias and stereotypes in large language models.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Gender bias and stereotypes in large language models

Reference 30

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This paper cites Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models

Reference 31

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This paper cites Evaluating the accuracy and reliability of large language models in assisting with pediatric differential diagnoses: A multicenter diagnostic study.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Evaluating the accuracy and reliability of large language models in assisting with pediatric differential diagnoses: A multicenter diagnostic study

Reference 32

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This paper cites Large Language Models are Geographically Biased.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Large Language Models are Geographically Biased

Reference 33

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This paper cites The imperative for regulatory oversight of large language models (or generative AI) in healthcare.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies The imperative for regulatory oversight of large language models (or generative AI) in healthcare

Reference 34

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This paper cites How AI is Shaking Up the Mental Health Community: ”Rather Than Pay for Another Session, I’d Go on ChatGPT”.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies How AI is Shaking Up the Mental Health Community: ”Rather Than Pay for Another Session, I’d Go on ChatGPT”

Reference 35

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This paper cites Automatically Interpreting Millions of Features in Large Language Models.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Automatically Interpreting Millions of Features in Large Language Models

Reference 36

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Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Race and Networks in the Job Search Pro- cess

Reference 37

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This paper cites About a Quarter of U.S.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies About a Quarter of U.S

Reference 38

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This paper cites A large-scale analysis of racial disparities in police stops across the United States.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies A large-scale analysis of racial disparities in police stops across the United States

Reference 39

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This paper cites Comparative perspectives on the regulation of large language models.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Comparative perspectives on the regulation of large language models

Reference 40

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Observation 443be122-a68c-4443-938e-4e0eabc10103 · outbound

This paper cites Racial disparities in school-based disciplinary actions are associated with county-level rates of racial bias.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Racial disparities in school-based disciplinary actions are associated with county-level rates of racial bias

Reference 41

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This paper cites Racist Cops, Vested “Blue.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Racist Cops, Vested “Blue

Reference 42

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Observation 8b0a2cff-a14c-4946-9dd7-9abf147ed8b5 · outbound

This paper cites The unequal opportunities of large language models: Examining demographic biases in job recommendations by chatgpt and llama.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies The unequal opportunities of large language models: Examining demographic biases in job recommendations by chatgpt and llama

Reference 43

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Observation 67aef869-e6cc-410c-b2b7-ef5d886b0abf · outbound

This paper cites How Implicit Bias Contributes to Racial Disparities in Maternal Morbidity and Mortality in the United States.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies How Implicit Bias Contributes to Racial Disparities in Maternal Morbidity and Mortality in the United States

Reference 44

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Observation 5723fdd1-5936-4127-ac1b-5e520d510564 · outbound

This paper cites Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contexts.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contexts

Reference 45

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This paper cites Toward expert-level medical question answering with large lan- guage models.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Toward expert-level medical question answering with large lan- guage models

Reference 46

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Observation 1f870782-eaa0-42e8-aec6-0df5edd89454 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies A Simple and Effective Pruning Approach for Large Language Models

Reference 47

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Observation 6bd868aa-8630-44f4-b283-6cb7a8a3b380 · outbound

This paper cites Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet

Reference 48

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Observation 179d210e-cf5b-4a60-95cf-72f4212d9d02 · outbound

This paper cites Executive Order on Safe, Secure, and Trustworthy Artificial Intel- ligence.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Executive Order on Safe, Secure, and Trustworthy Artificial Intel- ligence

Reference 49

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Observation 814365f6-278e-4f55-898a-1eb08e83e59c · outbound

This paper cites Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Reference 50

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Observation d8e5f6aa-d52f-44c0-ac30-c6b144a5d862 · outbound

This paper cites SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning

Reference 51

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Observation 7200a08c-27e5-4891-a564-699913e1474f · outbound

This paper cites Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-08T11:42:07.679330Z

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Unavailable: canonical work link unavailable.

Observation b4afc119-08f3-4a02-af84-5cdcdebfa7f4 · outbound

This paper cites Fairness & Privacy in an Age of Generative AI.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Fairness & Privacy in an Age of Generative AI

Reference 53

Resolution: verified exact; doi, observed 2026-08-08T11:42:07.952423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4fe29510-ecc0-4130-8b04-2c02d8433406 · outbound

This paper cites The Economics of AI Foundation Models: Openness, Competition, and Governance.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies The Economics of AI Foundation Models: Openness, Competition, and Governance

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:42:09.035592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fec0b0f7-f9cf-4c53-b0e6-b3a9515be053 · outbound

This paper cites Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-08T11:42:08.145561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 805eb223-68e8-495a-91c3-3f4b68c3aa32 · outbound

This paper cites Fairness-Aware Structured Pruning in Transformers.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Fairness-Aware Structured Pruning in Transformers

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-08T11:42:08.123284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 072f18dd-f583-4338-8081-d14c79fbb887 · outbound

This paper cites AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T11:42:07.714122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0869e5b3-4271-45b0-9692-6b34579f48e1 · outbound

This paper cites AIR-Bench 2024: A Safety Benchmark Based on Risk Categories from Regulations and Policies.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies AIR-Bench 2024: A Safety Benchmark Based on Risk Categories from Regulations and Policies

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-08T11:42:07.741050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfef0154-879c-405f-8ca5-81e6ee4e0862 · outbound

This paper cites Know what you don’t need: Single-Shot Meta-Pruning for attention heads.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Know what you don’t need: Single-Shot Meta-Pruning for attention heads

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:42:09.021819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 76e1ea91-8321-4d5b-bcf9-43ca1719f616 · outbound

This paper cites Revolutionizing finance with llms: An overview of applications and insights.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Revolutionizing finance with llms: An overview of applications and insights

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-08T11:42:07.782660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e1ec753-f4ac-457c-b7bf-4b128b75e9a5 · outbound

This paper cites an unresolved cited work.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-08T11:42:09.007382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b9631a8d-732b-4a7c-88a8-90246b2f8a5d · outbound

This paper cites an unresolved cited work.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-08T11:42:08.993369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f64e30ac-2013-4e4a-b97f-81bfde596962 · outbound

This paper cites These selected variations serve as the foundation for subsequent pruning experiments, allowing us to focus on cases where bias is most evident.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies These selected variations serve as the foundation for subsequent pruning experiments, allowing us to focus on cases where bias is most evident

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:42:08.979304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c0c3a3b7-6f93-497f-b5b5-53059a2b413f · outbound

This paper cites an unresolved cited work.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-08T11:42:08.963347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 863cd65b-94f1-4f66-9f1c-0bf49fed7f73 · outbound

This paper cites Empirical observations suggest τmin ≈ τmaj, leading to the choice of the following ranges.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Empirical observations suggest τmin ≈ τmaj, leading to the choice of the following ranges

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:42:08.948804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5be44567-958d-48fb-8b19-56792b247018 · outbound

This paper cites The resulting SMD for different parameter combinations is shown in Figure 5 (neuron pruning) and Figure 6 (attention head pruning).

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies The resulting SMD for different parameter combinations is shown in Figure 5 (neuron pruning) and Figure 6 (attention head pruning)

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:42:08.934048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0ba1eb53-40b8-4902-a8b2-e9d36f2fc1ff · outbound

This paper cites an unresolved cited work.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-08T11:42:08.917178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6a0997b7-eb52-4d16-ac00-b1f3f4cf2c1f · outbound

This paper cites Any response that falls outside this range is marked as a utility violation.

Breaking Down Bias: On The Limits of Generalizable Pruning Strategies Any response that falls outside this range is marked as a utility violation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:42:08.902500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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