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Paper Citation Record · LEDGER

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases

As of 11 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2501.03112.

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

pith.paper-citation-record.v1
2501.03112 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:56:29.947321Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

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  • verified fuzzy8
  • unresolved30
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4c9fda61-dc49-401a-9e11-4c24594b01e9 · outbound

This paper cites RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models

Reference 1

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Observation 9a8cf0d2-c30b-4544-9dd5-fd1948583f4b · outbound

This paper cites Unmasking contextual stereotypes: Measuring and mitigating bert's gender bias.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Unmasking contextual stereotypes: Measuring and mitigating bert's gender bias

Reference 2

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation cad1ae51-b69d-4b53-b338-f53e74001943 · outbound

This paper cites AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

Reference 3

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Observation f4ae09c9-1c15-4ea5-9dd0-9ffc067525ce · outbound

This paper cites Bring Your Own Prompts: Use-Case-Specific Bias and Fairness Evaluation for LLMs.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Bring Your Own Prompts: Use-Case-Specific Bias and Fairness Evaluation for LLMs

Reference 4

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source=arxiv_source observed=2026-08-10T21:56:29.682192Z digest=sha256:df9e596ee077a00bec63e73f384a07edf0d027ab70d9fca63678125feb203fbd

Observation a7b08a19-fa73-42fe-9faa-a04f14db746f · outbound

This paper cites Measuring fairness with biased rulers: A comparative study on bias metrics for pre-trained language models.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Measuring fairness with biased rulers: A comparative study on bias metrics for pre-trained language models

Reference 5

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Observation 6ccbd285-ea54-4a27-aa4c-199f5cc41bc8 · outbound

This paper cites Bold: Dataset and metrics for measuring biases in open-ended language generation.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Bold: Dataset and metrics for measuring biases in open-ended language generation

Reference 6

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Observation f7e64131-58ca-4fd9-8ce2-1c141981ef97 · outbound

This paper cites WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Models.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Models

Reference 7

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Observation 3a690209-2f9a-48e4-bd28-6d01caf4ec52 · outbound

This paper cites Bias and Fairness in Large Language Models: A Survey.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Bias and Fairness in Large Language Models: A Survey

Reference 8

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Observation 4bdd822a-57a9-4424-b93d-41a57a1ebc84 · outbound

This paper cites A framework for few-shot language model evaluation, 07 2024.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases A framework for few-shot language model evaluation, 07 2024

Reference 9

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Observation 5565d9f3-385c-4908-9b50-741e18d0bd7a · outbound

This paper cites an unresolved cited work.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Unresolved cited work

Reference 10

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Observation 00efa17b-4fdc-406d-b8ca-a9836e3f6977 · outbound

This paper cites Intrinsic Bias Metrics Do Not Correlate with Application Bias.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Intrinsic Bias Metrics Do Not Correlate with Application Bias

Reference 11

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Observation 97821cb3-e1e0-47f9-a9b7-3a3469659a34 · outbound

This paper cites Reducing Sentiment Bias in Language Models via Counterfactual Evaluation.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Reducing Sentiment Bias in Language Models via Counterfactual Evaluation

Reference 12

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Observation d2119278-9c00-4249-a600-99afeee24c86 · outbound

This paper cites TrustGPT: A Benchmark for Trustworthy and Responsible Large Language Models.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases TrustGPT: A Benchmark for Trustworthy and Responsible Large Language Models

Reference 13

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Observation 68680030-ec2d-4e23-8e31-0b53ccdcf196 · outbound

This paper cites an unresolved cited work.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Unresolved cited work

Reference 14

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Observation 52e182ca-f00b-4c40-93d4-9c2f3d14b018 · outbound

This paper cites Github - huggingface/evaluate: Evaluate: A library for easily evaluating machine learning models and datasets., 2022.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Github - huggingface/evaluate: Evaluate: A library for easily evaluating machine learning models and datasets., 2022

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T21:56:29.765396Z digest=sha256:b08c179b1ce8cc8bbb9227c55025013169aee5bc1995a5d401db4629ba48bed1

Observation 8cf5299e-17f9-466f-a770-c3dd834d990a · outbound

This paper cites Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems

Reference 16

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Observation 0ac66915-078e-400a-9b3e-3fce3a818d18 · outbound

This paper cites Grep-biasir: A dataset for investigating gender representation bias in information retrieval results.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Grep-biasir: A dataset for investigating gender representation bias in information retrieval results

Reference 17

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Observation 6891c092-a895-4fd4-8ea7-1a7739c0315f · outbound

This paper cites Collecting a large-scale gender bias dataset for coreference resolution and machine translation, 2021.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Collecting a large-scale gender bias dataset for coreference resolution and machine translation, 2021

Reference 18

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Observation e3adfad6-cde4-4695-b635-57663b786b73 · outbound

This paper cites UNQOVER ing stereotyping biases via underspecified questions.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases UNQOVER ing stereotyping biases via underspecified questions

Reference 19

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Observation f7e3189a-48ed-4eda-8eb0-f730cbd65023 · outbound

This paper cites Holistic Evaluation of Language Models.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Holistic Evaluation of Language Models

Reference 20

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Observation 89d26502-b61d-4007-93fa-428b87b9dd57 · outbound

This paper cites Stereoset: Measuring stereotypical bias in pretrained language models, 2020.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Stereoset: Measuring stereotypical bias in pretrained language models, 2020

Reference 21

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Source-reported events for the cited work

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Observation 18cd6dcc-ee37-4e1e-aa1a-2a595e4a6a10 · outbound

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LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Unresolved cited work

Reference 22

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Observation fe8eae3e-1ac9-4348-8c14-3c81d75e7f9b · outbound

This paper cites LangTest: A comprehensive evaluation library for custom LLM and NLP models.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases LangTest: A comprehensive evaluation library for custom LLM and NLP models

Reference 23

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Observation f0107dff-e375-4869-8871-434c589edeab · outbound

This paper cites HONEST : Measuring hurtful sentence completion in language models.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases HONEST : Measuring hurtful sentence completion in language models

Reference 24

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Observation f9f01f4c-772b-44fb-8dbe-3fffea2c6aaa · outbound

This paper cites BBQ : A hand-built bias benchmark for question answering.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases BBQ : A hand-built bias benchmark for question answering

Reference 25

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Observation 715ad8aa-5dfd-4830-97f4-399bde5a72f1 · outbound

This paper cites Perturbation Augmentation for Fairer NLP.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Perturbation Augmentation for Fairer NLP

Reference 26

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This paper cites Gender bias in coreference resolution.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Gender bias in coreference resolution

Reference 27

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Observation 3945aa4d-f4e7-4e0e-8665-948ad43b9c7d · outbound

This paper cites Aequitas: A Bias and Fairness Audit Toolkit.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Aequitas: A Bias and Fairness Audit Toolkit

Reference 28

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Observation c129e532-98a2-453f-8f25-817e4d59fbd4 · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 29

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This paper cites GitHub - tensorflow/fairness-indicators: Tensorflow's Fairness Evaluation and Visualization Toolkit , 2020.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases GitHub - tensorflow/fairness-indicators: Tensorflow's Fairness Evaluation and Visualization Toolkit , 2020

Reference 30

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This paper cites LiFT : A scalable framework for measuring fairness in ml applications.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases LiFT : A scalable framework for measuring fairness in ml applications

Reference 31

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Observation f61ec470-37ac-4b88-ab3e-a865ba2cf371 · outbound

This paper cites Decodingtrust: A comprehensive assessment of trustworthiness in gpt models.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Decodingtrust: A comprehensive assessment of trustworthiness in gpt models

Reference 32

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Observation a8907c67-a482-424d-afb0-4518a25e16f5 · outbound

This paper cites Mind the GAP : A balanced corpus of gendered ambiguous pronouns.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Mind the GAP : A balanced corpus of gendered ambiguous pronouns

Reference 33

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This paper cites Fairlearn: Assessing and Improving Fairness of AI Systems.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Fairlearn: Assessing and Improving Fairness of AI Systems

Reference 34

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This paper cites The What-If Tool: Interactive Probing of Machine Learning Models.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases The What-If Tool: Interactive Probing of Machine Learning Models

Reference 35

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Observation 7dfe604c-d1e4-4e6f-8dac-f54b617b2dce · outbound

This paper cites Towards Auditing Large Language Models: Improving Text-based Stereotype Detection.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Towards Auditing Large Language Models: Improving Text-based Stereotype Detection

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-10T21:56:29.929671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T21:56:29.929671Z digest=sha256:963e66c4187511fcf8e384faba4ad1d0b0fd1c16efd5682f4d51369655518bde

Observation 5c395c50-cdcf-4224-aa1f-9bf090041aaf · outbound

This paper cites Is chatgpt fair for recommendation? evaluating fairness in large language model recommendation.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Is chatgpt fair for recommendation? evaluating fairness in large language model recommendation

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-10T21:56:29.938931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T21:56:29.938931Z digest=sha256:18577bcbc476af7cdd4c1e013a845b450c90ff125ebb3cd4435186ef8be22647

Observation 84a317fc-c196-4ab8-bdb7-09fb59ce67ea · outbound

This paper cites Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods.

LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-10T21:56:29.947321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T21:56:29.947321Z digest=sha256:8aa9f2c6a9aa1d48ab77cc54579b2e4f38c9eaaf1cadde27a1d703a0735fd7ff

Pith citing papers

No inbound Pith citation observations are available.