Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-02T14:50:22.053224Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 0 inbound Pith citation observations for arXiv:2607.22546.
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, observed 2026-08-02T14:50:22.053224Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
Source: cited_works
78 of 78 outbound references displayed
External citation measurements
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Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation
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Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the G e NTE Corpus
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Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution Inseq: An Interpretability Toolkit for Sequence Generation Models
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Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution A decade of gender bias in machine translation
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Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment
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Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution https://aclanthology.org/2020.eamt-1.61/
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Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution Gender Bias in Contextualized Word Embeddings
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Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution Proceedings of the 5th Conference on Machine Translation (WMT) , publisher=
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Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution Gendered Technology in Translation and Interpreting , pages=
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Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution Mind the Inclusivity Gap: Multilingual Gender-Neutral Translation Evaluation with m G e NTE
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Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution Gender bias amplification during Speed-Quality optimization in Neural Machine Translation
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Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution Analyzing Gender Translation Errors to Identify Information Flows between the Encoder and Decoder of a NMT System
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Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution Context-Aware Neural Machine Translation Learns Anaphora Resolution
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