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Found 20 papers, 3 papers with code
Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy
The goal of question answering (QA) is to answer _any_ question.
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
We take human annotations as a gold standard and show that a correlated automatic metric is suitable for development.
Query Refinement Prompts for Closed-Book Long-Form Question Answering
Large language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts, both in few-shot closed-book settings.
Flexible text generation for counterfactual fairness probing
A common approach for testing fairness issues in text-based classifiers is through the use of counterfactuals: does the classifier output change if a sensitive attribute in the input is changed?
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Scaling language models with more data, compute and parameters has driven significant progress in natural language processing.
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Toward Deconfounding the Influence of Entity Demographics for Question Answering Accuracy
The goal of question answering (QA) is to answer any question.
How to Write a Bias Statement: Recommendations for Submissions to the Workshop on Gender Bias in NLP
At the Workshop on Gender Bias in NLP (GeBNLP), we'd like to encourage authors to give explicit consideration to the wider aspects of bias and its social implications.
They, Them, Theirs: Rewriting with Gender-Neutral English
Responsible development of technology involves applications being inclusive of the diverse set of users they hope to support.
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains.
Measuring and Reducing Gendered Correlations in Pre-trained Models
Pre-trained models have revolutionized natural language understanding.
Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias
The one-sided focus on English in previous studies of gender bias in NLP misses out on opportunities in other languages: English challenge datasets such as GAP and WinoGender highlight model preferences that are "hallucinatory", e. g., disambiguating gender-ambiguous occurrences of 'doctor' as male doctors.
Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation
Machine translation systems with inadequate document understanding can make errors when translating dropped or neutral pronouns into languages with gendered pronouns (e. g., English).
Social Biases in NLP Models as Barriers for Persons with Disabilities
Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models.
Automatically Identifying Gender Issues in Machine Translation using Perturbations
The successful application of neural methods to machine translation has realized huge quality advances for the community.
Gendered Ambiguous Pronoun (GAP) Shared Task at the Gender Bias in NLP Workshop 2019
The 1st ACL workshop on Gender Bias in Natural Language Processing included a shared task on gendered ambiguous pronoun (GAP) resolution.
Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns
Coreference resolution is an important task for natural language understanding, and the resolution of ambiguous pronouns a longstanding challenge.
A Challenge Set and Methods for Noun-Verb Ambiguity
English part-of-speech taggers regularly make egregious errors related to noun-verb ambiguity, despite having achieved 97{\%}+ accuracy on the WSJ Penn Treebank since 2002.
