Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2110.08527.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T21:54:44.064381Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T19:03:39.513287Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 0120ee3b-f2bc-41bb-b8d2-b35ff506f87e · inbound
Challenges in Guardrailing Large Language Models for Science cites this paper.
Challenges in Guardrailing Large Language Models for Science An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T21:54:44.064381Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 460f0386-c0a1-49de-9ce1-110c8fb804b9 · inbound
Bias Unveiled: Investigating Social Bias in LLM-Generated Code cites this paper.
Bias Unveiled: Investigating Social Bias in LLM-Generated Code An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:46:44.570628Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6714122a-a1f8-4f8e-b121-a1186bb6a614 · inbound
CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models cites this paper.
CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T12:32:13.548435Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 183d18d1-28ae-426e-a6a2-9cdb9b84da9e · inbound
Social Bias in LLM-Generated Code: Benchmark and Mitigation cites this paper.
Social Bias in LLM-Generated Code: Benchmark and Mitigation An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
Reference 149
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T15:36:08.804040Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 65d66880-c10b-4c26-8ab5-6a9cc0ca15d5 · inbound
DebiasRAG: A Tuning-Free Path to Fair Generation in Large Language Models through Retrieval-Augmented Generation cites this paper.
DebiasRAG: A Tuning-Free Path to Fair Generation in Large Language Models through Retrieval-Augmented Generation An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:03:39.514963Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.