REVIEW 11 cited by
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
read the original abstract
We introduce a new benchmark, WinoBias, for coreference resolution focused on gender bias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing coreference benchmark datasets. Our dataset and code are available at http://winobias.org.
Forward citations
Cited by 11 Pith papers
-
GKnow: Measuring the Entanglement of Gender Bias and Factual Gender
Gender bias and factual gender knowledge are severely entangled in language model circuits and neurons, making neuron ablation an unreliable method for debiasing.
-
StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs
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.
-
StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs
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.
-
Social Bias in LLM-Generated Code: Benchmark and Mitigation
LLMs show up to 60.58% social bias in generated code; a new Fairness Monitor Agent cuts bias by 65.1% and raises functional correctness from 75.80% to 83.97%.
-
SCOPE: A Dataset of Stereotyped Prompts for Counterfactual Fairness Assessment of LLMs
SCOPE is a new large-scale dataset of counterfactual prompt pairs for evaluating fairness and stereotype sensitivity in LLMs across 1,438 topics, nine bias dimensions, 1,536 groups, and four communicative intents.
-
Speak Your Mind: The Speech Continuation Task as a Probe of Voice-Based Model Bias
The authors perform the first systematic bias evaluation in speech continuation tasks across three models, revealing gender interactions in text metrics and stronger reversion to modal phonation for female prompts.
-
Navigating the Sea of LLM Evaluation: Investigating Bias in Toxicity Benchmarks
Toxicity benchmarks for LLMs produce inconsistent results when task type, input domain, or model changes, revealing intrinsic evaluation biases.
-
Gemini: A Family of Highly Capable Multimodal Models
Gemini Ultra reaches human-expert performance on MMLU for the first time and sets new state-of-the-art results on 30 of 32 benchmarks, including all 20 multimodal ones tested.
-
Text and Code Embeddings by Contrastive Pre-Training
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.
-
Representational Harms in LLM-Generated Narratives Against Global Majority Nationalities
LLMs generate narratives containing persistent stereotypes, erasure, and one-dimensional portrayals of Global Majority national identities, with minoritized groups overrepresented in subordinated roles by more than fi...
-
DebFilter: Eradicating Biases Stashed in Value
DebFilter mitigates biases in text-to-image diffusion models by applying a fixed offset to the guidance embedding slice in cross-attention during inference.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.