Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2010.06032.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:12:25.231885Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T10:27:56.884365Z
0 of 0 outbound references displayed
External citation measurements
No source-named external measurement is stored.
No outbound reference observations are available for this paper version.
Observation a3e725d0-7ee9-404d-8c8f-b7bece085abc · inbound
ART: Automatic multi-step reasoning and tool-use for large language models Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 96
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e94d7d02-17a2-4a2f-8877-f53054e2abdb · inbound
StarCoder: may the source be with you! Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 265
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 536320ea-f51b-4697-b77b-f16e69b76bfb · inbound
TrustLLM: Trustworthiness in Large Language Models Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 174
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 6a941e34-c576-48c6-bbe9-993aba3eb63a · inbound
Mitigating Extrinsic Gender Bias for Bangla Classification Tasks Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 36
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 8e2057fe-491b-407c-b73c-9f7c21eb4626 · inbound
Bias in Large Language Models: Origin, Evaluation, and Mitigation Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 73
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 91b9fee3-05ff-4342-92ce-8b9e21dd9b7b · inbound
Relative Bias: A Comparative Framework for Quantifying Bias in LLMs Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 72
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0f165937-d155-42e6-a762-c1d75940fb27 · inbound
Advertising in AI systems: Society must be vigilant Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 20
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c7269381-3974-4ce4-aa7b-b97e18cab81d · inbound
Paying Alignment Tax with Contrastive Learning Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 30
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cdbb6702-9ac1-46e7-a791-956bd5f48434 · inbound
Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 51
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8fa49dc5-bcb0-457b-add9-ee2069436618 · inbound
Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 54
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 12aee931-70de-4b26-bde7-bbf320d41e1a · inbound
FairI Tales: Evaluation of Fairness in Indian Contexts with a Focus on Bias and Stereotypes Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 54
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2b51e76e-6fcf-4b29-b8fc-8659b12d50a5 · inbound
From Measurement to Mitigation: Exploring the Transferability of Debiasing Approaches to Gender Bias in Maltese Language Models Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 26
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b3dab8cd-b76d-4df0-8597-301f1f636e9e · inbound
KLAAD: Refining Attention Mechanisms to Reduce Societal Bias in Generative Language Models Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 37
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 14a23f3d-a247-4604-8cf6-f3bb52951f0d · inbound
No LLM Solved Yu Tsumura's 554th Problem Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 32
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a21c4733-c3db-4fa1-8abe-e399068601ca · inbound
CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 42
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 539bc654-8786-4e9f-a9cc-209e1f2aaa82 · inbound
Can We Trust a Black-box LLM? LLM Untrustworthy Boundary Detection via Bias-Diffusion and Multi-Agent Reinforcement Learning Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 38
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e6bddfa8-9a48-4ef6-aca3-e0d0c42b92c3 · inbound
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 61
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5a854764-f808-4ebe-a4cf-cd52414ed4ad · inbound
StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 113
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 43e5508c-fa84-4cae-8aff-95ecb3dd9740 · inbound
StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 113
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 0397a2e1-802a-4e21-8eaf-3a1e7007b029 · inbound
DebiasRAG: A Tuning-Free Path to Fair Generation in Large Language Models through Retrieval-Augmented Generation Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 61
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 68d91503-4689-46b4-95a0-123ba797fd99 · inbound
Debiasing Without Protected Attributes: Latent Concept Erasure from Textual Profiles Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 68
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 42495f78-5459-4bbb-86fe-7db80ebfaa70 · inbound
Position: It's Time to Optimize LLMs for Self-Consistency Measuring and Reducing Gendered Correlations in Pre-trained Models
Reference 65
Source-reported events for the cited work
Unavailable: canonical work link unavailable.