pith. sign in

arxiv: 1804.09301 · v1 · pith:SH3L4D6Pnew · submitted 2018-04-25 · 💻 cs.CL

Gender Bias in Coreference Resolution

Rachel Rudinger , Jason Naradowsky , Brian Leonard , Benjamin Van Durme This is my paper
classification 💻 cs.CL
keywords genderbiascoreferenceresolutionsystemsconfirmcorrelatediffer
0
0 comments X
read the original abstract

We present an empirical study of gender bias in coreference resolution systems. We first introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender. With these "Winogender schemas," we evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems, and correlate this bias with real-world and textual gender statistics.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Language Models are Few-Shot Learners

    cs.CL 2020-05 accept novelty 8.0

    GPT-3 shows that scaling an autoregressive language model to 175 billion parameters enables strong few-shot performance across diverse NLP tasks via in-context prompting without fine-tuning.

  2. StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs

    cs.CY 2026-05 accept novelty 7.0

    StereoTales shows that all tested LLMs emit harmful stereotypes in open-ended stories, with associations adapting to prompt language and targeting locally salient groups rather than transferring uniformly across languages.

  3. StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs

    cs.CY 2026-05 unverdicted novelty 7.0

    StereoTales shows that LLMs produce harmful, culturally adapted stereotypes in open-ended multilingual stories, with patterns consistent across providers and aligned human-LLM harm judgments.

  4. Flamingo: a Visual Language Model for Few-Shot Learning

    cs.CV 2022-04 unverdicted novelty 7.0

    Flamingo models reach new state-of-the-art few-shot results on image and video tasks by bridging frozen vision and language models with cross-attention layers trained on interleaved web-scale data.

  5. Compared to What? Baselines and Metrics for Counterfactual Prompting

    cs.CL 2026-05 conditional novelty 6.0

    Counterfactual prompting effects on LLMs are often indistinguishable from those caused by meaning-preserving paraphrases, causing most previously reported demographic sensitivities to disappear under proper statistica...

  6. Parcae: Scaling Laws For Stable Looped Language Models

    cs.LG 2026-04 unverdicted novelty 6.0

    Parcae stabilizes looped LLMs via spectral norm constraints on injection parameters, enabling power-law scaling for training FLOPs and saturating exponential scaling at test time that improves quality over fixed-depth...

  7. Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models

    cs.AI 2024-08 conditional novelty 6.0

    Empirical analysis shows scaling inference compute via strategies like tree search can be more efficient than scaling model parameters, with 7B models plus novel search outperforming 34B models.

  8. Text and Code Embeddings by Contrastive Pre-Training

    cs.CL 2022-01 unverdicted novelty 6.0

    Contrastive pre-training on unsupervised data at scale creates text and code embeddings that set new state-of-the-art results on classification and semantic search benchmarks.

  9. Bias in Large Language Models: Origin, Evaluation, and Mitigation

    cs.CL 2024-11 unverdicted novelty 2.0

    A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.