Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs | Paper Citation Record

Observation f4ad1b80-b063-46dc-88c3-6877f0fb3775 · outbound

This paper cites LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Education.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Education

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:00.547421Z

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Unavailable: canonical work link unavailable.

Observation a7ae2f7f-52ca-41dd-992e-f5e282c0bc5c · outbound

This paper cites Sociodemographic biases in medical decision making by large language models.Nature Medicine, pages 1–9, 2025.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Sociodemographic biases in medical decision making by large language models.Nature Medicine, pages 1–9, 2025

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:00.642738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64702c07-eb10-4786-83fe-33706bccf8a2 · outbound

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

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Gender bias and stereotypes in large language models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:00.726192Z

Source-reported events for the cited work

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Observation dd299b5c-523d-484a-bb2d-a23b61552c82 · outbound

This paper cites an unresolved cited work.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:00.799747Z

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Observation 2793897f-bc9d-47b4-85d6-5fdce9e313ee · outbound

This paper cites StereoSet: Measuring stereotypical bias in pretrained language models.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs StereoSet: Measuring stereotypical bias in pretrained language models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:00.877995Z

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Observation c7ceb052-d9db-4f2e-96b6-c9a524555705 · outbound

This paper cites Gender bias in coreference resolution: Evaluation and debiasing methods.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Gender bias in coreference resolution: Evaluation and debiasing methods

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:19.497520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 31e7f150-9993-48c1-99b0-6db77dd0dce9 · outbound

This paper cites Bowman, and Rachel Rudinger.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Bowman, and Rachel Rudinger

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:19.267473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 468a81f8-8f8a-4b7c-820d-ae5be38d70c1 · outbound

This paper cites Bias and volatility: A statistical framework for evaluating large language model's stereotypes and the associated generation inconsistency.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Bias and volatility: A statistical framework for evaluating large language model's stereotypes and the associated generation inconsistency

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:19.055690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cb5013cd-9271-4a2c-beb9-f917c3345a18 · outbound

This paper cites Climb: A benchmark of clinical bias in large language models, 2024.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Climb: A benchmark of clinical bias in large language models, 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:18.863505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b7901ef5-813c-4963-bc2b-86d56270b81d · outbound

This paper cites an unresolved cited work.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:20:18.725073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5cbefb64-df8c-4b5d-82de-bdb8c26c6eda · outbound

This paper cites On measuring and mitigating biased inferences of word embeddings.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs On measuring and mitigating biased inferences of word embeddings

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:18.505746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b162f64a-fb29-4873-a52a-f026a218c528 · outbound

This paper cites Cai, James Wexler, Fernanda B.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Cai, James Wexler, Fernanda B

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:18.215788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 923d0997-97cf-4627-a2ea-18dd84139fc0 · outbound

This paper cites Controlling Large Language Models Through Concept Activation Vectors.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Controlling Large Language Models Through Concept Activation Vectors

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:01.561067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7efb8429-131f-4ba5-9ae0-ed4562b10300 · outbound

This paper cites Can sparse autoencoders be used to decompose and interpret steering vectors?.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Can sparse autoencoders be used to decompose and interpret steering vectors?

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:20:08.798186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e1f4484-a231-460e-a265-3d2da723df71 · outbound

This paper cites Sparse autoen- coders find highly interpretable features in language models.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Sparse autoen- coders find highly interpretable features in language models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:17.967898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b807e74d-1396-4002-a249-b0b7748c0440 · outbound

This paper cites Scaling and evaluating sparse autoencoders.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Scaling and evaluating sparse autoencoders

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:17.692089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3931ee01-8fe7-4374-bedb-ca7879aa8b36 · outbound

This paper cites People’s perceptions toward bias and related concepts in large language models: A systematic review, 2024.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs People’s perceptions toward bias and related concepts in large language models: A systematic review, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:17.448547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1d368a0-d868-4cb4-be3d-a2efe2029b22 · outbound

This paper cites Bias and volatility: A statistical framework for evaluating large language model’s stereotypes and the associated generation inconsistency.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Bias and volatility: A statistical framework for evaluating large language model’s stereotypes and the associated generation inconsistency

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:17.202212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 196b4eef-41b3-47ff-bc70-d1e553ac3f13 · outbound

This paper cites an unresolved cited work.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:02.043258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04535dbf-9c3e-46e0-ba63-7360885f3bd1 · outbound

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

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs A Survey on Fairness in Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:02.182984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc9338c7-a20b-4192-bcec-fab2aac14f32 · outbound

This paper cites On measures of biases and harms in NLP.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs On measures of biases and harms in NLP

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:16.929480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f72d6afa-0acd-4f13-9e25-41f4b42306dc · outbound

This paper cites an unresolved cited work.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:20:16.706950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6d4d4480-14cc-42ba-88a2-57ec271d297f · outbound

This paper cites Exploring value biases: How llms deviate towards the ideal, 2024.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Exploring value biases: How llms deviate towards the ideal, 2024

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:16.460787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4d59ea4d-6325-4434-a53c-9c4448a73a25 · outbound

This paper cites Writing style matters: An examination of bias and fairness in information retrieval systems.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Writing style matters: An examination of bias and fairness in information retrieval systems

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:16.213708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4d2936b0-5ef0-4cbb-bcd6-b1537157e2c0 · outbound

This paper cites Bowman, and Shi Feng.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Bowman, and Shi Feng

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:15.985878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2da4fb4f-01a3-49a4-bf5b-db08997de7c6 · outbound

This paper cites Measuring Gender and Racial Biases in Large Language Models.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Measuring Gender and Racial Biases in Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:02.607082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a0786be-ebbe-4313-8008-e241ec9fe2b7 · outbound

This paper cites you gotta be a doctor, lin.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs you gotta be a doctor, lin

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:15.744741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ce88de44-4e22-4ada-88b3-94d2794bd172 · outbound

This paper cites Justice or prejudice? quantifying biases in LLM-as-a-judge.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Justice or prejudice? quantifying biases in LLM-as-a-judge

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:15.597119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 212416b2-1c3e-4372-b6fb-4854cbcbd125 · outbound

This paper cites Large language models propagate race-based medicine.NPJ Digital Medicine, 6(1):195, 2023.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Large language models propagate race-based medicine.NPJ Digital Medicine, 6(1):195, 2023

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:15.400019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation db00568b-a719-46da-8b25-2af189aa41f3 · outbound

This paper cites Unmasking and quantifying racial bias of large language models in medical report generation.Communications Medicine, 4(1):176, 2024.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Unmasking and quantifying racial bias of large language models in medical report generation.Communications Medicine, 4(1):176, 2024

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:15.297091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 74713fda-d252-4608-aba3-2578966488b6 · outbound

This paper cites Racial differences in pain assessment and false beliefs about race in ai models.JAMA Network Open, 7(10):e2437977–e2437977, 2024.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Racial differences in pain assessment and false beliefs about race in ai models.JAMA Network Open, 7(10):e2437977–e2437977, 2024

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:15.146727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 836c5452-c9e6-4b6d-8f8c-53523c363556 · outbound

This paper cites Bowen III, S.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Bowen III, S

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3817eda4-f558-490b-924a-c57ab93e96f8 · outbound

This paper cites an unresolved cited work.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:20:15.023501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c2d0c1fb-b4d0-4d5e-93d0-8a99637f9357 · outbound

This paper cites Evaluating large language models: A comprehensive survey, 2023.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Evaluating large language models: A comprehensive survey, 2023

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:14.863194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d0f0f41e-3120-4663-adc3-f3ab04ab1dac · outbound

This paper cites StereoSet: Measuring stereotypical bias in pretrained language models.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs StereoSet: Measuring stereotypical bias in pretrained language models

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:14.719173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f5f7f12c-1f3a-4f07-84df-9a325ea89f79 · outbound

This paper cites Unmasking the mask–evaluating social biases in masked language models.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Unmasking the mask–evaluating social biases in masked language models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:14.604051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6d9fc246-5c49-4798-a4da-c3253b63687e · outbound

This paper cites Measuring bias in contex- tualized word representations.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Measuring bias in contex- tualized word representations

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:14.507832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9e1ed8c3-5404-4b2e-9563-b418b4f4f373 · outbound

This paper cites On Measuring Social Biases in Sentence Encoders.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs On Measuring Social Biases in Sentence Encoders

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:03.591577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 027be15c-9bf4-42c0-9537-8a2448acdec4 · outbound

This paper cites Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:14.388421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1688abc9-8bca-482e-9532-a17e7ef9d1d1 · outbound

This paper cites Semantics derived automatically from language corpora contain human-like biases.Science, 356(6334):183–186, 2017.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Semantics derived automatically from language corpora contain human-like biases.Science, 356(6334):183–186, 2017

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:03.800610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a872e9c5-fa64-47b7-a94b-199164059e74 · outbound

This paper cites Understanding the origins of bias in word embeddings.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Understanding the origins of bias in word embeddings

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:14.249781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation edfd8ca7-2ae3-42c4-b1d8-3c48c9f6f6ec · outbound

This paper cites Explaining explainability: Recommendations for effective use of concept activation vectors.Transactions on Machine Learning Research, 2025.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Explaining explainability: Recommendations for effective use of concept activation vectors.Transactions on Machine Learning Research, 2025

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:14.119705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6f0dfbab-7f4e-4008-91a5-22591d8e0733 · outbound

This paper cites Uncovering safety risks of large language models through concept activation vector.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Uncovering safety risks of large language models through concept activation vector

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:13.904754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6d6b8023-3909-417d-ad62-39726bf54795 · outbound

This paper cites Controlling large language models through concept activation vectors, 2025.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Controlling large language models through concept activation vectors, 2025

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:13.756586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a3d7366-0902-4529-ae3f-2a9ebfd2f7f2 · outbound

This paper cites Steering llama 2 via contrastive activation addition, 2024.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Steering llama 2 via contrastive activation addition, 2024

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:04.250065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac641339-517f-4bf7-8bc8-e17021c09b83 · outbound

This paper cites Steering llms’ behavior with concept activation vectors, September 2024.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Steering llms’ behavior with concept activation vectors, September 2024

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:13.573209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9fd72358-a208-4c59-acf5-86519dfc2ce1 · outbound

This paper cites Extracting unlearned information from llms with activation steering, 2024.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Extracting unlearned information from llms with activation steering, 2024

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:13.445695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4d16da9a-b89f-4a37-a89d-1685aacce044 · outbound

This paper cites Sparse autoencoder.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Sparse autoencoder

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:13.240066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e7ef55ad-c58d-488b-9385-733d134f5de8 · outbound

This paper cites Efficient training of sparse autoencoders for large language models via layer groups.arXiv preprint arXiv:2410.21508, 2024.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Efficient training of sparse autoencoders for large language models via layer groups.arXiv preprint arXiv:2410.21508, 2024

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:04.661721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8142bc8-b6d2-4b4c-b1ed-8b14e2b723c0 · outbound

This paper cites Efficient Dictionary Learning with Switch Sparse Autoencoders.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Efficient Dictionary Learning with Switch Sparse Autoencoders

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:04.734941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea790f9a-95ee-4c67-9ad7-c6dc333ae882 · outbound

This paper cites Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:04.815226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf2d2942-bd61-4e48-874b-8c5cd02f6412 · outbound

This paper cites Smith and Jonas Brinkmann.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Smith and Jonas Brinkmann

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:13.043591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a6ae5d75-4d93-470f-832f-9bcaa61200cd · outbound

This paper cites Effectiveness of sparse autoencoder for understanding and removing gender bias in LLMs.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Effectiveness of sparse autoencoder for understanding and removing gender bias in LLMs

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:12.870268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a2e9bdb6-113a-4c73-b1a0-c1b0cf904dde · outbound

This paper cites Daniel Freeman, Theodore R.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Daniel Freeman, Theodore R

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:05.119169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf572a05-0572-4963-ad15-a33f2c5999a0 · outbound

This paper cites Man is to computer programmer as woman is to homemaker? debiasing word embeddings.Advances in neural information processing systems, 29, 2016.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Man is to computer programmer as woman is to homemaker? debiasing word embeddings.Advances in neural information processing systems, 29, 2016

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:05.225203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3474fa47-f57d-4889-9bfa-1b561c1ed5e2 · outbound

This paper cites The woman worked as a babysitter: On biases in language generation.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs The woman worked as a babysitter: On biases in language generation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:12.695950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 620aeb6c-d15b-4309-b639-a82853e515d4 · outbound

This paper cites an unresolved cited work.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:20:12.525438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 24fa8342-0790-47e5-96c2-abeec8c540e9 · outbound

This paper cites Openwebtext corpus.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Openwebtext corpus

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:05.507440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c298123b-8fe8-4739-afa4-804ee70969f5 · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:05.603592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f210cb3f-ab2a-409a-80d2-484f0b6ea586 · outbound

This paper cites Zico Kolter, and Matt Fredrikson.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Zico Kolter, and Matt Fredrikson

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:05.706978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b612988-66d0-4866-a5f1-02a48a352f5b · outbound

This paper cites k-sparse autoencoders, 2014.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs k-sparse autoencoders, 2014

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:12.320831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 813f385b-0cff-426c-85ff-f26b889889b1 · outbound

This paper cites Neuronpedia: Interactive reference and tooling for analyzing neural networks, 2023.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Neuronpedia: Interactive reference and tooling for analyzing neural networks, 2023

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:12.164939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6eaf3b3c-5947-4f7d-b98e-07f9f957a464 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Gemma 2: Improving Open Language Models at a Practical Size

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:06.065137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c1ab923-aa46-4c3f-bf81-46af245e588d · outbound

This paper cites The Llama 3 Herd of Models.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs The Llama 3 Herd of Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:06.179251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ff7fe80-b3ad-41f2-b408-5e3b2c762780 · outbound

This paper cites an unresolved cited work.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:20:12.030126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c5a5e8f3-991c-4e7d-905f-d86863b12d42 · outbound

This paper cites Zhang, Federica Sarro, and Mark Harman.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Zhang, Federica Sarro, and Mark Harman

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:11.803827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 140d703d-0c83-42f2-87f5-2b949eb53955 · outbound

This paper cites Reducing sentiment bias in language models via counterfactual evaluation.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Reducing sentiment bias in language models via counterfactual evaluation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:11.628366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation abb754a3-02ef-4ea5-aa4c-753874bff4f7 · outbound

This paper cites A survey on fairness in large language models, 2024.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs A survey on fairness in large language models, 2024

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:11.444859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1860bf70-8767-41fa-af10-640dd16438c7 · outbound

This paper cites Character-level convolutional networks for text classification.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Character-level convolutional networks for text classification

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:06.654615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b92a1cb-8426-48c4-9770-299b59f7f835 · outbound

This paper cites Maas, Raymond E.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Maas, Raymond E

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:11.242679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b18efa03-cde5-40bb-b62c-50e0ffe2966a · outbound

This paper cites Explore spurious correlations at the concept level in language models for text classification.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Explore spurious correlations at the concept level in language models for text classification

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:11.082183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0844a647-bc78-4fb2-8c56-fabfc41897b8 · outbound

This paper cites RedditBias: A real-world resource for bias evaluation and debiasing of conversational language models.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs RedditBias: A real-world resource for bias evaluation and debiasing of conversational language models

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:10.924090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation de8b85d3-988f-4473-8771-d68df06b0eef · outbound

This paper cites Towards detecting unanticipated bias in large language models, 2024.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Towards detecting unanticipated bias in large language models, 2024

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:10.768383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bc306fe6-1b00-48dd-9378-40a5b3346924 · outbound

This paper cites Edu-values: Towards evaluating the chinese education values of large language models, 2025.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Edu-values: Towards evaluating the chinese education values of large language models, 2025

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:10.597164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8b1ee7a9-8d95-4072-a113-af7e3cf2556f · outbound

This paper cites Evaluation and mitigation of cognitive biases in medical language models.npj Digital Medicine, 7(1):295, 2024.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Evaluation and mitigation of cognitive biases in medical language models.npj Digital Medicine, 7(1):295, 2024

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:10.393321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation da3b8bdc-b572-4e2d-aa75-73a19107daf2 · outbound

This paper cites Socioeconomic status and mental health — Wikipedia, the free encyclopedia,.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Socioeconomic status and mental health — Wikipedia, the free encyclopedia,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:10.224860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 537ec2a7-4cc5-456f-8ec7-cd14618c3ee0 · outbound

This paper cites Glover, Diana M.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Glover, Diana M

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:09.801096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 59ed34cc-04b1-46bf-8c83-c083c8fc4212 · outbound

This paper cites Pedregosa, G.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Pedregosa, G

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:07.886644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44e39811-7b05-45d9-b76e-3025a1623229 · outbound

This paper cites Lg-cav: Train any concept activation vector with language guidance.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Lg-cav: Train any concept activation vector with language guidance

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:09.575238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 669b531c-bffd-430a-85b4-81b7ddeb85b4 · outbound

This paper cites describe.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs describe

Reference 80

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T15:20:09.324746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c949f6e9-4d21-42f6-a8af-620d20106d86 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:20:09.078803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dfb16f97-4aa3-45ac-b4aa-b18cb41f62a3 · outbound

This paper cites an unresolved cited work.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Unresolved cited work

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:03.319821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0841c85-6ba3-4146-b003-644cf2d6faa4 · outbound

This paper cites an unresolved cited work.

Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:20:10.072279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers